{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance (2nd ed.)\n",
    "\n",
    "**Mastering Data-Driven Finance**\n",
    "\n",
    "&copy; Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/py4fi_2nd_shadow.png\" width=\"300px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics (c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from pylab import mpl, plt\n",
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.style.use('seaborn')\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "np.random.seed(1000)\n",
    "np.set_printoptions(suppress=True, precision=4)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unsupervised Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets.samples_generator import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_blobs(n_samples=250, centers=4,\n",
    "                  random_state=500, cluster_std=1.25)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TpmVoooABFhERAbC+bl5lMLNupfPhMCdDaoW5Ih594jgOnxpa8uVvdz1A1e8ZWbW8bVEJB7e4NZvSyfuEaRmaKPC8ZYQQvwrg3QDOAyhJKT/h9T6JiMg6s3k2YayHpCohsGfnJlv5Q+nuJDra4paGB89fnMHD+46Fok284nfpjKjy9K8uhEgC+FsA/01K+XEA1wsh3unlPomIyB6zeTZ285m8UmsIEICt/KFkIobrFbMl9WhLQwfdJhQ8r8PqWwCclVJemv/3IQB3ebxPIiKyqVZStdv1kOyWTahkZgjQbrL4vXcKrOlN2j421ohqXF4PEa4EMFXx78n5n+lavjyJWKzF40NyTzrNLlIjbBs1to8a28eYH23zwD03ITOTx/mxLFb2JJFqiy+89uq5cWUwU2xuNnWM+UIRX3zyJI68/CbGJnPo6Uzg5i2rcN/u6xE3WGDZSLI9gZ7OBMYmc0te6+lM4JoNaaTa4srPpfLoR96Jv/6nF/Hs8ddRmLN0aJbaxA9hOY4wcrttvA6wzgOoPOLO+Z/punhxaT2VsEqnOzAyMlV7wwbEtlFj+6ixfYz53Tbtrc3ITueQnb4cuMSKJWU+U3OxaOoYHztwelHO1NhkDgcOn8VMLm9r2ZWtG3p06zxt3dCz5DPofa5ainNFy8EVAMRjTSjmC6E4p3ltGbPbNqqgzOshwucArBdCLJv/99sBfMvjfRIRhZobw2JBcaMekhfLrjit86Rit1wDAOQLJTz57KDjY6Do8bQHS0qZFUL8IYD/KYQYAXBSSvl9L/fZKMyu3k5E4RHG2Xd2OK2HZLdsgoobdZ6M2CnXUMnKgspUPzz/a0spDwI46PV+ghBEkFMvN2iiRqQqJWBnWCwoTushebnsihclBFTHa4ZbS+dQtDCctkFV0M7rIKdebtBEjcZuNfEwsxvM+LEGoZsPwKrjbY01Y7ZQRFcqjuylAvKF0pJtuFZfY4rW1RwSQQU59XiDJmoUXgyLRZlXy6541ctvdLy7b9uA8elLWNHZhid+eMaXhaspGvgXtyjIIIc3aKLo8nJYLIq8WnbFqwdg1fF2JsuJ9VyrjyoxwLIoyCCHN2ii6PJjWCyK3MyZyszkPX8A1o5XmwlaOQTJtfqoEv/yFgUZ5NTjDZqzIamRsIfDW8NjGc8fgM0MQRoFjdX3O97/6hv/ohY5DXKcXlD1coPmbEhqROzh8NbqnpTnD8B2hiCX3O9SrWhLxJDN5TGRyfP+V6d4Zduwd9dmtCXiurMIjbgVUNTLDZqzIamReVFKgIBUW9zTXn67ObhL7neZWYxnLgeBvP/Vp+h9M4eAnYJ2XgQUpeLS6cBO+NVdzdmQROQVL3v57eTgWqkC78f9j8OS/mHrOmD2KdTNgMJpT5jexeX3cB1nQxKRV7zs5beTg2ulCryX978g6zc2KgZYPnAzoLDbE6YKovweruNsSCLymhfDsHZycK1Ugffy/se0DP8xbPWBdoHpsXJBOVkgVbu4tItcu7geOyBdX3S1FjcWiyUitSgvKB1me3dtxo6+taYXlVbd76p5df/zYnFtqo3fZD5wq7yC3Z4w1cV18swopmbylt+z8r1V4/lGr9fLbEiisOEMXW/ZGYJccr9TzCL0AtMygsEAyyduBBR2h9ZUF9fUTB7tbTFMzyx9glG9p+omXuv12XwRI+NZ7Nm5CXt2bor0bEiisOFQkD+sDEEaBWV+zQZnWkYw+I3mEzcSL630hFX2HNW6uK67qgeH+odrvmcl1U38gXtuMnx94BfjmLlU4JM1kQc4QzfcqoMyv8p11GOR6ihgq/pMtcxCLYW5IorFEuKxpoUV21tjzfiV61bV7DnaurEXz54YWvKelQGO2d61WjfxkYtZw9eHLmQX/jvqT9ac7kxhw6Gg+uVGkWqr9RvJGX4r+MxJfsT+gwN49uTiIGm2UERzU9PC7xr1HN16/Rrs6FtruF8rvWu1buKnXxszPS0ZcO/J2q+AhzkuFFYcCqo/bhaptlq/kZxh6/rMbn6E2VkgRtv0D17Awx/crsx5MttdXesmfs1VPaanJQPOn6z9DniY40JhxaGg+uP2/YarCPiHj9s+cjJV1kzXv5ltkokY1q3scHSjrVVmIb08aXpaMuD8ydqoBMX+gwO239MIpztT2FktI0DhxftNtPFxxiVmhqec5EeY7fr3a3ig1qxIvdfblsUW5WBpnDxZ+53UyxwXqha2XLx6Wa+UeL+JOl51DlkZnnKSH2G269+v4YFaN3G911vjzcrSDnb4fQNijgtpwp6Lx6Gg6OP9JtoYYDlkZXzcaX6EmVpaXhXwNHpKr3UTr37d7Sdrv29AzHEhDXPxyGu830Qb/zoO2Bme2rtrMwpzRZw4M4rpmYKlAMhM17/bwwNePKW7+WQdxA2IVeipEetNhW0otFHwfhNdvEocsDo8pQUrL702humZAjqScVx3Va/lYMVMgOJWEBOFp3S3bkBmv0BiLc3Ys3MTtm9ZhaZm4IoVziYNUPQ0Um5M2IdC6x1z6qKLfyUHrA5PVQcrU9k8DvUPIdbSFJpgpVJUntKd3oCsfIHwy4aAxsqNicJDViNgTl308BvBgVrlCqqXrjEKVo6/MhKa6bZahXmtN6fWU7qT93eb3RIUVso8+FkSgsLLyrUfZSwTQGRffdwFAmR2eEoVrExk8vjKd17G7//6WwLrBdHrmbnuql50peKYyOSXbN/d3orUsrjp5X7C2vNjpZcuKj165I9GyI1ppKFQIrfx28Ahs8NTqiEFADgqR7H/4EBgXe56wwCH+oewpjepG2C1LYvhkX0vmA6WwjrMYOULhF82VKkRcmMaaSiUyG0cInRJreGpZCKG667qVb7H8VdGIH9+0fdud1XPzEyugLdvXb2oKvSa3iSGLmRND5OFeZhB+wLRU/0FYmVbahxurI4QVo0yFErkBV4dPtCGx04N6gcZmolMHp/+2ou+D58pe2Yys9h105V43zs3Y3RyBqllcTyy7wXdbY2CpTD3/Fgp88CaNNSIGmEolMgLnn0jCCE+ByALYBrANgAfllIOe7W/MHvswGkc6jf/0f0YPqssSWBmGECbwXJ2eLJmsLQeyxf93K1hBq/q8Fj5AuGXTfSxnpM1jTAUSuQFL6+SjJTyQQAQQvwJgI8B+C8e7i90CnNFPHZA4scWgqtKXiROGyWbb93Yi2dPDC3Zvrpnxk6w5LTnx+sEeStfIPyyia6wTrSICpYJILLGs7uKFlxV7Gfaq315JZsrQP58zHZe1P6DAzjUP4SSzf3bLYVQ65j0ygygBOzoW7so12pH39olPTN2czL27tps6v2tHLPbpRGs5NLUc95NvWKJDSLyk6NvByHEUwBW6bz0kJTym/PbdAP4NQDvqfV+y5cnEYu1ODkkV+QLRfztN07gmRdfx6V8EQCwLN6MHTdcgT949zbEY+W4NJ02fprLzOTRPzim3M/yjmX40Ht/GZ9//EVcnLq05PWezgSu2ZBGqi3u4NOYO6aXfnYRf/OR2wEA58eyWNmTNNzvh953I9oScRx5+U2MTebQ05nAzVtW4b7d1yvb5oF7bkJmJl/z/c0ec//gGJLtCdfax0+qc4fcbx8/z6PMTB7DYxms7kl5cm7Wahuv9x92vLbU2D7G3G4bRwGWlPIO1etCiC4AfwPgd6WU6mgDwMWLWSeH45rHDpxeMpx1KV/Ed3/yc1y6VMC9d16DdLoDIyNThu9xdngSY5M55X6u39iLdSuSuH5jr+7w2dYNPchO55CdVr+PWapjGpvM4fTgCNat7EB7a/Oi/erlrPzmzo24e/v6RcNk4xczAFCzbarf341jjpJa7dPovGgfP84jP4YgVW1jZ//1lo/Ga0uN7WPMbtuogjIvk9xXAPgrAH8spTwnhHiPlPIbXu3PLdlcAcdfGTF8/fgrI9izc1PN91HlKjUBeNvW1QvDY34lTlvNn6p1w/YjJ4N1eMiIleDAj/Mo6FpvVvbPfDQi73n5yPLd+fffL4QAgCkAoQ+wRsazuoU1NROZvO5MuWqqxO63bV2DD9x17cK/9RKnAeDcyLSrT5ZWk82D/sIAWBqBlrITHHh9HgVd5d/q/sNwbRPVO8+ueCnlDV69t5fS3UnD5WEAoCsVN/20a7VnKpmIYW085emTpdljcvqF4ebQA0sjUCW7wcHeXZtRLJXwk5fexGyhnFsZjzWhWCyhMFd0dH0FXevNyv6DDgaJGgWvoirJRAx9V6d1n3QBoO/qtO7NRy+gMJrSn80VDHunvH6yNFtmwO4XRmGuiEefOI7Dp4ZMBYhmAjGWRogOr3N6nAQHsZZmNDc1LQRXAJAvlPDsySE0NzcZXl9mPpOdIUizbVW5nREr+w86GCRqFPyW0vHe2zdB/vwihscul0hobioP7VX3mpgZrtBylcp1sU4bbuvnk2Wt/Cm7OStmA0S7wzy88YeTXzk9ToIDq9eXlc9kZQjS7Pvqbbf9LWvw7luvcrR/t/PR6i1RnsgtvBp0PP70mUXBFQAUS0BLc9OSG5vZgCKbK+Ar3/4pjg6MGG4bpidLOzkrVr7AmANSX/z6ezoJDqxeX1au7ZHxLHbftgHA5aHsrlQcG/9N18LPrb6v3nYHDp/FTC6v26Zmh9LdykdjojyRGgOsKlaCBDPbtsabsf/gAI6/MmKY16VtG7YZc++9fRMGfjGO4QtZlFCe/bi6N4n33q4/i9LsF5hqpmaQOSB8ErfH755Xu8GBlevLyrVdHWA8+J/eiq9//xWceX0cxwZGMfjGkYXAYzZfNNVWdtrUylC6G3mNfEgiUuO3SBUrT7lmtv3BsXOG+VxL3ndlh6kvD78CgcefPoOhC5drk5UADF3I4vGnz+jeQM18gRXmivjKt39qGGwGkQPCJ3Fn/O55tRscWAnO7FzbWoAx8IvxRddNZeCxs2+tqbZy0qZmhtKd5jUyUZ6oNl4BVaw85aa7k+hOtWI8o7NtqhWpZXHDm1ClypmJqi8PPwMBOzdQM19gjx04vWiYtFoQPXV8EnfG755XJ8GB2eCs1mdqRhOOSf3zePiCfsHkE2dGcfctv2SqrfxqUyt5jZUPdmFKZyAKKwZYVaw85SYTMbQlYroBVlsihumZWcObUKVMLo8vf/tl/Kc7r0FnstXwy6O6wnytnBAnPVx2b6B7d21GWyKuO4tQFbRp/K5txSdx54KqVWZn0oPZ4Ez1mdqWxfCZrx/D9Iz++qRGa4+OT88icylv+L7XXdW7cCxhqv+m92B33VU9xg+XLABMBIABli4rtaKyOf2hrmwuj/a2VsOn0EqFOeDYwChODf4Yt7ylXOG9+svDSU6I1R6uwlwR3zv6Opqg/2WhuoHGWppx/56+JcvoAOXCqaq2uFH4X9uKT+LuiFqtMjPBmd5nalsWWzT8p6fWdbPwvq+MYjwzu7D9qcFRPHbg9ML1qrd/bRahU1YewvR6eA/1D2NNb1I3wGIBYKIyXgU6rNSKMsolmsjklU+remYLRcOhKSc5IXrvp7L/4AAO9Q8bvm7mBqr3BaYa9uhKxfH+d23xPecpbBMLoqoea5VVf6bUsjge2fdCzd9b3ZvUDcIqr5t777wGfzf3Mg71Dy8EYxOZ/KLrVa9N11+53NFaclbTDCYzs/jxqSHd98rm8nj71jV46bULkQiqifwW7Tugx2o95ba3taK9LaY7VLDkaXX+hma0fSW9oalagYAq38vKUJeqp6y8huLSWmBmqYY9jAq4ei1MQzH1oB5rlWmf6ezwpLIHtiMZxw2b03jv7Zvw+NNnlL152VwBL702pvs+1derm21qNd9w31MS+YL+oOdEJo9dN12B973z6roJqoncxKvBhspq5UbBUvXTavVTsOpGrTc0VSsQUOV7WRnqUvWUlQDsuukKR71MesMe113Vi1u3rg4s3ylqw1sUDNVDTntbDA/dexN6uxIAULM3z2nBVDs5llbzDbO5As6cGzd8P21yTj0G1URuYIBlQ/VTYCVVcT/tJlRr2NBoaEoVCMzmi64MdS3vTKA11rxoORE772OkctjjzYsZPH3sHF567QIO9Q8FVh6hHoe3yDyzAYvqIedGsXIhuKrc3ijwsDM0XZgr4q/+8RiOnBrG1Eze8vViNajcLxRCAAAgAElEQVSrtfD9piu6eJ0QKfDqsEj1FNiRjONj97x1yY22mhYo/fjUkG73u9HQlCoQiLU0K2c9tcbNBSxP/mhQN7hSHZcdyUQMPzoxtCjXK+jyCHwSbyx2yp641dtpdWi6MFfEn335iGF9LTPXi9WgTrV9a6wZ99zBEiZEKgywLFI9BU5ly4ntvVAHWFqgtPvWDdj3XYkzr49jIpM3fbM2CgT27tq8pMghUC4Ouv/gQM2bsCp4jMeasPvWDbqv2cHyCBQ0O/XP3OztrBWsVfasff37A4azF81eL1aDOtX2t7xlNTqTraY+J1Gj4jeYRW7OOutMteL+3VtdG5qazRcxc0k/J8zMTVgVPOYLJYxnLqEz5c5NleURKEhOA3w3ejuNgrXqReG7UnFkcsYTY/SuF6NhT6s9cMxPJLKPAZZFXsw6c2toymnQ4mfJApZHoCD5FeCbye+qvv6re9ZUeVAA0N4Wx0wub6oWntUeOOYnEtnHK8UGVbXyIDkNWvwsWcDyCBQkrwN8u8tamVntoNql2QI+/bXjuoVQjYY9rT7U2X0I5ALq1Mh4xtugqlYeJDeCFj+HBDj8QEHxOsC3u76lqmfNSH6utLAPo989cWYUFyZymJ6Z9SXY4QLqRAywHAnjrDOnQYvXQwLVT7QcfqCgeBXgO8nvqjVzL9kWx/jUJaQSLZjNFxeCq1rGp2fxyceex1S2dnkHN3qduIA6EQOsuuNWgOR28Kh6og1joEr1z61rpTogcZLfVWvm3h/+hz6cHhzBTK6AT3/tRUvHOZXNLxyDXrBTq9fJbODFGcJEZTzLPRCGvIOwBS18oqWwsnutGAUku2/bYNgLFY81oTu1TPm+qp61VFsc61Z2IJsrmFpIXqU62DG6RoulEpqbmkwP93GGMFEZAywXMe9AH59oqR6pHhq2buzFsyeWLpKcL5Tw5LODCw8Veg9jZnrWVD1da3qTmLlUqLn2aWWwo7pGf/LSm4uKD9d6OOIMYaIyfqu5iL00+vxedy0MPYhU32o9NFx9Rbfh7544M4rJ7Cye/NGg8mEsmYhhBdoMz+VaS2fVWvu0MthRXaNGKzsYPRxxhjBRGc90lzRqL42ZYMbuumtWewPZg0h+qfXQcOq1C4a/Oz49i31PncZRObroZ5UPY0bn8ofed+PC79RaOqvW2qeVwY7qGlV9jtdHprH5yqXBJGcIEzHAck2j5R2ogplqdp5o7fQGsgexMQXRY6kKSFTDcgCQSsRw8ox+AKY9jD3xwzO653JbIo7f3Llx0e/UyiHbfesGTGfzOHPOeEku1TUajzXprpkKAF/45370XZ1e8hDDAqVEDLBc02h5B6pg5oF7blqyvZUnWju9gY3ag9jIJrOz+PsDp/HquYklgYPXPZa1Hhpeem3M8IFrWWuL4dI3Wq+Q0bl85OU3cff29abO5eqHoK5UHDeKFbjnjmt01xE0ukaLxRKePbk0nwwoV5lXPcQENdmGaQIUBjzzXNJIeQe1gpnMzNKlPaw80drpDWy0HsRGpgUOz50atpR87TbVQ0P1A4hm1fI2zFwyXvqmKxVHqVQ0PJfHJnOmz2W9JXeOylG0tw3qto9qbcTm5iYcf2XEcNmesDzE5AuL13FkmgAFqX6+9UOgUfIOagUz58eyaG/Vv5mZeaK10xvYaD2IjcwoeNH49WWvemiovhe0t5UfwN7RtxYP7ztm+J6brujGlSs7Dc/lns6EqXPZSY9u9TWqfc7tW1bi0187rvs7YXmI+eKTJ5kmQKHBAMtFjZJ3UCuYWdmTRHY6Z/v97fQGNlIPYiMzs1af31/2eg8NRvcCVf2qeKwJ9/yaUJ7LN29ZZepc9qJHd01vu2E+VhgeYrK5Ao68/Kbua2HpYaPG4nmfqRDiQSGEtdVLQy6bK+DVc+PIGuRRJBMxrFvZUbcXs/YFoGfbphVItcUd72Pvrs3Y0bcW3e3lXJHu9lbs6Fur7A208zsULWbW6gvDl72m+l6gunbe9pY16EyVz12jc/m+3deb2q/2EKTHbvs8+eygYbJ7GB5iRsazGJvUf7DTgkoiP3l6RQghdgJY7uU+/MQyAJd5PRxqpzewUXoQG5mZcgJh+LJXMXPtGJ3L8ZjxfaY6sdtsj66ZhHBVz2FrrBm7b9tg6rN7Kd2dRE9nQjfIClPQTY3Ds7uQEGIVgP8I4FMA7vVqP35iGYDL/Apm7MxCCtsyQeSeWuUE3vaWNaHvsbRy7Zg5l40e/N57+yYAxoGclQfGWoVIx6cv6c5M9FMyEcPNW1bhwOGzS14Le9BN9cnRGSeEeArAKp2XHgLwGwAeANBl9v2WL08iFmtxckieyczk0T84pvta/+AYku0JV4bGomi9TidlOs0AR4Xto6Zqnw+970a0JeI48vKbGJvMYXnHMlx7VQ/+6N3b0NWhXufPqsxMHsNjGazuSXlyfetdO7VUt82jTxw3rJn1wD03ITOTx/mxLFb2JBd9BtXv3b+nb9E+ku0Jw96hns4ErtmQDsX9TxtC1c6Nns4Ebt6yCvftvl7Z+9dIeO8x5nbbOAqwpJR36P1cCPFWAHkAv4/yEGGbEOKjAL4hpXzF6P0uXsw6ORxPnR2eNBzfH5vM4fTgCNat5IkLlE/SkZGpoA8jtNg+amba5zd3bsTd29cv6gGazc1iJGd/8eNKYU0HqG6bbK6Aw6f0a1QdPjW0UDOrvbUZ2encwuQTs79XaeuGHt2ew60beha9d5DS6Q7dc2P8YiboQwsF3nuM2W0bVVDmSZ+plPIFAC8AgBDilwB8QEr5KS/2ZZXdAnQsA0AULnaHgs3cA6KSDmB3tqCd34tSGRqmCVAYeJ3kvgnAH6Dcg/UggM9JKQN5lHD6RMoyAETRZvYeEKVVAew++Nn5PU4iIbLG06tDSnkG5TysB7zcjxluPJFG6QmOiBYzew+I0qoAdh/8nDwwsneIyJyGePxw64m08gmu2NyM5mKRT3AhwvXHyIiVe0DU0gHsPvjxgZHIWw3xLeT2E2kyEWOyYIiENSGZwsPKPSDM6QCZmTzODk8ueoiwMnRX/RDCIT8i7zTE1RT0Eyl7VrwVlYRkCo7Ve0DYene0h4j+wTGMTeZ0HyJUQ3eqhxAO+RF5oyG+7YN6ImXPiveilJBMwbF6DwhbQrfThwg+hBD5r2G+eYJ4IuVNzXtRSkimYNm5B4Shd8fpQwQfQoiC0TBXld9PpLyp+SPo4V+KjrD1SplV6yHi9ZFpbL6y2/bv8yGEyBsNN05Vvbq9V8zc1Mg5behHT9AJyRROft0D3KI9RBj5wj/347EDp1GYK1r+fb8fQrK5As4OTyKbK/i2T6KgROMOE0HsWfFP2BKSidykyh8DgIlMXpl6EIZZkcxHpUbEAMsjYbipNYqoDv1QuIVp9q/2sHDy1Qu4OHVJdxtV6kHQDyHMR6VGxG8hDwV9U2s0YUhIpugLY2+L9hDx5tQl/H+PHtLdRpVPFeRDCPNRqVHxrPYQe1aIoifMvS1XrekyTD3oSsUxkysoA5YgHkKYZE+NioPfPohaUi1RozLT2xKkVFvccFLHzKU5fPprL+LBLx1WJr37LUxJ9kR+YoBFRDQvCrN/9+7ajB19axeClnisCQAwWygHVFqP2/6DA6bez4+ZfRvXdun+nPmoVM94ZhMRzYvC7N/K1IPXR6fwhSdPYaKQX7Jdrfwmr3PNqt8/HmtCE5owWygyH5UaAgMsIqJ5UZr9m0zEsCzWgonM0uAKUOc3ZXMFfPnbL+PYwOii7d3MNavOZcsXSgBKuFGswPvftSVUbUnkBZ7hREQVojT712qPm9ardPyVEcPAzI2ZfapctlfPTdp+X6IoYYBFRFQhSrN/rfa4Vfcq6XFjZh9nDtafMNWFiwq2EhGRjqjUVTPb46bqVarkRq5ZFHLZyJww1oWLCgZYREQRZrbHTdWrVMmNXLMo5bKRWpjrwoUdz/IGxe5eovpSq8dN1asElAuV9l2ddi3XLEq5bKSPVfidYcs0GHb3EvkjbA8xql6lG0Ua73/Xta4eZ5Ry2Ugfc+mc4dneYNjdS+StMD/EqHqVrBybleAxKrlstBRz6ZxhgNVA2N1L5L0wP8Q47VUKc/BI7mMunTNsnQbC7l4ib0XlIcZur1KYg0fyBnPp7Av+SiffsLuXyFv1/BATleCR3MVcOvvYp9tAtO5ePezuJXJOe4jRE/WHmCgshE3eSSZiWLeyg98TFjDAajB7d23Gjr61C18C3e2t2NG3lt29RC6o54eYeg4eibwQ3audbGF3L5G36jVnhQnPRNZ4dkUIIVoB/L8AMgC2ALggpfyYV/sjazh1msgb9fwQU6/BI5EXvLzq/wTAM1LKHwGAEOJ6D/dFRBQq9fgQU8/BI5HbvLwyfgvAz4UQNwDoBfDXHu6LiIh8Uo/BI5Hbmkqlku1fFkI8BWCVzksPAXgcwO9LKf9eCPGrAB6UUu5UvV+hMFeKxVpsHw8RERGRj5oMX3ASYKkIId4EcJuUUgohEijnYrVKKeeMfmdkZMqbg/FAOt2BkZGpoA8jlNg2amwfNbaPMbaNGttHje1jzG7bpNMdhgGWl2Uavg9gw/x/rwfwqiq4IiIiIqoXXuZg/TGATwgh+gBcC+C3PdwXERERUWh4FmBJKc8B+KBX709ERET2ZXMFjIxnke5OcjaoB9iiREREDaQwV8T+gwO69cxiLVzgxS1sSSIin2VzBZwdnkQ2Vwj6UKgB7T84gGeOv7GwtuT49CyeOf4G9h8cCPjI6gt7sIiIfMKeAwpaNlfAiTOjuq+dODOKbK7A4UKX8IomIvIJew4oaCPj2YXzr9r49CxGJ2d8PqL6xQCLiMgHZnoOiLyW7k6iu71V97Xu9las6Gzz+YjqFwMsIiIfsOeAwiCZiGHrxl7d17Zu7OXwoIsYYBER+YA9B8GK6sQCT47baM2UyKylEg0MVYmIfJBMxLBt0wo8c/yNJa9t27SCPQceierEAq+OO5sroH/wgu5r/YMXmOTuIrYiEZFP9u7aDAC6X5rkDW1igUabWAAA9955TVCHVZNXx21mqHpdosP2+9NlDLCIiHwSa2nGvXdeg2yugNHJGazobGNvgYeiWpLAy+PWhqr1giwOVbsrvP2jRER1KpmIYd3KjlB+udeTqE4s8PK4taFqPRyqdhcDLCIiqktRnVjg9XHv3bUZO/rWLuyju70VO/rWcqjaZQxViYioLkV1YoHXx82han+wRYmIqG6FaWJBNlfAyHgW6e5kzYDGj+NOJmJMaPcQAywiIqpbRr012VwB50amTQU7TtkpucBepujjX4uIiOqe1ltTmCvisQOnPamLpddDlc0V8OVvv4xjA5dnBVopucBepuhigEVERA3Di/pSej1UWzf2AiXg5KujmMjkdX/P71IRmZk8zg5P+tJrRwywiIioQXhVX0ovaHv2xFDN3xufnsXro1PYfMVy5XZWcrf0aAFg/+AYxiZzkalmH3UMsIiIqCF4UcVcFbSZ8YUnT6Hv6rRusOPWcjlRrWYfdQxdiYjIV0EtvOxFfSlV0GbGRCaPZ46/gf0HB5a8pgVG2vtrgZHetkbM9NoFJaoLcJvFHiwiIvJF0Asve1FfSrX0jBXVQ5S1AqMLEzlMz8zWHDYM49qDQZ8HfmGARUREvgjDUJXb9aVUQZsV1cHOL85PKQOjT3z1CKZnCsrgpDBXxPeOvo4mACWd9wmqmn0YzgM/MMAiIiLPhWXhZTfqS1UnnesFbdoswv7BCxifnkV7WwzTM8ZDYR3JOFZ0ti307hx/ZUR5DNp7qYKT/QcHcKh/2PA9gqhmH5bzwA/18SmIiCjUwjZUZae+lGpoqzpoA8qf+dfffhUyl/JILYvjkX0vGLbB9RvLwc5jB07b6g2zMsTYBOBtW9cEUs0+bOeBlxhgERGR51S5SkEMVdkpfVBraCuZiGFtPGUYhBkNJa7pTWLPzo2QPx9T9ly1J+KYzunX1KoOTlSBTAnArpuuCCTfKWzngZcYYBERkWl2azL5tfByreOzm2BtdmhLFYRVDyW2t8WwdWMvYs3N+PhXjtRMlP/gr1+Lr377tKngJKyBTFQX4Lajfj4JERF5xo2ZX14uYFyYK+LRJ47j8Kkh5fHZTbA2M7S1Am3KIGzPzk1LhhKf+OEZU0OC3e2t2LS223RwEuZAJkwLcHuJARYREdXkxswvLxcwNnN8ThKszfQImc0v0vK/rBQp1YIiK8FJ9bY9nQls3dBjKpBxWj1epVEWsq6/T0RERK5ye+aX2wsYmz0+JwnWZnqErA7LmSlSWh1AWQlOqre9ZkMa2encwut6QZSfNarqfSFrzwIsIcSNAD4K4AUAvwLgL6SUz3m1PyIi8kbYZ36ZPT6neUm1eo+sDsupjqcrFccf/vutuCLdrhtAWQlOtG1TbXFkp3PKIKpRalT5wcserIcBfFFK+aQQYjeATwLY5eH+iIjIA2FNmNaYPT6neUlmeo+sDOGpjqfv6jQ2X9mtPB67jIKowlwJL712Qfd36q1GlR+8bKk3AaTn/zsN4KiH+yIiIo+EOWEasHZ8biRYq3qPrOYX+Z3wnc0VDEtBnDwziqkZc2Ugqt/Tq3ytKGsqlfQK6JsjhHgKwCqdlx4CcAzAPwI4DOAWAP9VSqkMsgqFuVIs1mL7eIiIyBv5QhFffPIkjrz8JsYmc+jpTODmLatw3+7rEY8Fv35c9fF1plpx05ZVuP8/9OkeX2Ymj/NjWazsSSLVFg/giP0/nnyhiL/Y9zyeO2Vc3b0zGcdkdmmQ1dOZwN985PZFx2b3nMjM5DE8lsHqnlQo2t6hJsMXnARYKkKIYwDul1I+J4TYCuB7AFZLKQ13ODIy5c3BeCCd7sDIyFTQhxFKbBs1to8a28dYGNomzDO/upen8Jf/8DxOnBmtuU5fI/qnH76KA4fPGr7e3d6K667q0V1eZ0ff2iU5WEZV5/W2BcK9yLPdayud7jAMsLy8Oq4EMDT/30MAlnm4LyIi8kGYZ3598cmTi4IDJmhfls0VcOTlN5XbVAY7tYYs7cwsbbQEei8DrPsA/A8hxEkAWwC8X9V7RUREZJcqgGCCdnmm5dhkzvD1G0V6Ibgyk0NmdWZpIy3yrPHs00gpnwTwpFfvT0REpFEFEGEoJRG0dHcSPZ0J3TbqSsXx/nddu2iYrlZPpZs1v+r178NBaSIiijwtgNAThlISXsjmCjg7PIlsrlBz22Qihpu36M1JK5eEsNp7pM3c1KOq+aWnXv8+9dUfR0REDUkLIPSSuMNQSsJNdpPF79t9PWZyeddKQrhV86ve/j6a+vtERETUkNwOINxkp1ZUNlfAL85PASjhypWdC79nN1k8HnN3DcCw1/wKGgMsIiKqC24HEGbUCpzs9DYV5orY912Jn7z0JmYLxfnP1oTtW1bjPTs3Ok4Wd3smqNn3a5RFnjX1+8mIiKgh+VFKwmzgZKe3af/BATx7YmjRz/KFEp49OYRMLh/5ZPEwl/pwE5PciYiILNICJy3Y0QKn/QcHFrZRLUtz/JUR3eR01e8AwJnXx9GV0q9+Xq/J4lHFAIuIiMgCMzWdgHJpgomM/tp+E5k8Ridnlvxc9TsAMJktYNMV+otAm00WtzL7kOzjECEREZEFZms6tbe1ogmAXoXtJgDNaMLZ4clF+Vvp7iS6UnHDIKsrFcc9vybQ3hY3VW29Mj8sXyjisQOnQ7lUTT1igEVERGSB2SKb0zOzusEVUA66PvP1Y0vWTEwmYui7Oq1bzgAo16zqTLUqk8WN8sOWtcYaaqmaoDFkJSIiz9TjcJTZIpvp7iS6U/rFNQFgeqbcJtX5W3t3bcat29YgHrv8FR2PNeHW69cs6qVKJmJYt7LDcM2/6vywZ158Xfc4Koc1yT3swSIiItfZLYYZFdU1nbpScWz8N13YfduGhW2SiRi2Xa1fXFOPFui0xpvR3NSE5LIWTBSKaG+LYdumNO65Q9RsO1V+2KV8UffnUZl9GDUMsIiIyHV2i2FGhVbTaTIzi33flTjz+jiODYxi8I0jiwLJ6kCsoy2OqRn9/Cot0PnBsXOL2m56poBD/UOItTTVbDtVfpgRzj70BgMsIiJylaoX5ag8j7tv+SX0dumvG+jmMVitnG7Hk88O4qi8XFahOpCsLq6ZWhbHI/teMMzfSi2Lm56hqPfZVPlhy+LNur1Y9bpUTdDYokRE5CpVL8r0TAGffOx53LA57clwoZ9Dk2bKNWiBS2VxTdWafNMzs8oZil/5zk/x6rkJw8+mWvNvxw1X4NKlQsMsVRM0BlhEROQqVS8KAExl854NF/o5NGm2XEN1b5pqTb7ZfNGw7eKxJmVvmcbo/f/g3dswfjHTMEvVBI0tS0RErlL1olQyu3aeWZmZvON1+swqzBXxvaPnDOtcdbe3oju1zLDulFGZhVhLs2HbNRnsrfqzGa35p81K9HqpGr+GZ8OucT85ERF5RutFOSrPL5QjqOb27LXhsYxv6/TtPziAQ/1Dhq9v27QCTz47qOxN0wt0srkCbt26GoW5Ek4NjmIik1+YoXhsQD94NPpsfq/5V+8zR61qvE9MRESe03pR/ux3bkZH0p+181b3pNDdrl93ys19qXKvmgC8fetq7L5tg6lkdU1hrlxl/cEvHcbD+47hyE+HF23TtixmWFOrCcDB519HYU6/DINfzKzP2EgYYBERkWd6uxK4YXNa9zW3Z6+l2uKmCoA6pcq9KgHYddOVuDiZq9mbVqk6OMkXSsjPlYcDJzJ5HOofRpvB8ZcAHOofCjSQMbs+YyNhgEVERJ7au2szdvStXehd6m5vxY6+tZ7MXvNjX1oSvx6tp8zMNhpVcFIpm8vj5mv1g1Ug2EDGTMJ/o2EOFhERecoo6Tqq+1Il8Vf2lJnZBjBfHHQik8dUVr9IKRBsRXaz6zM2EgZYRETkCz+Trr3el6rUgpVtgNplLTRdqTjOjUwbvh6PNaO7fZndj+SI2aCzkTTeJyYiInLITE+Z2d40s2UtVDMJASBfKOLJHw0GthSR2YCyUTDAIiIiMlCrppOZnjIz21QHJ/FYE5rQhNlCcSFQ2X3bBgy+cUTZ0+V2vS8r/BwKjoLG/eRERFTXnBS89Lumk15wAmBJoFKrp8urPCwrbel3/a2wYoBFRER1xY3gyM8ldypVByfVgcreXZtRmCvhx/1DhhXk3UwoZ/FQ+xhgERFRXXEaHFlZxNlvsZZmfOCuawGUcKh/eMnrbieUBxVo1gOGn0REVDfcKHgZhZpO9955jef1vlg81Bn2YBERUd0wExzVyg+KQk0no5ytcyPTri2y7EZbNjJHfwEhRDOA3wPw5wBul1KeqnjttwH8MoA5AK9KKf+3k30RERHV4kZwFKWaTslEDGvjKU/ypKIQaIaZ0yHCbQB+AiBb+UMhxBUAHgDwgJTyIwA+KIS42uG+iIiIlLTgSI+V4MjP5X2c8mqRZbfaslE5ah0p5YsAIISofukOAEellNokh+cAvAvAK072R0REVIsbBS+jUtPJ64R8Fg+1r2arCyGeArBK56WHpJTfNPi1lQCmKv49Of8zpeXLk4jFWmptFhrpNMeejbBt1Ng+amwfY2wbNa19HrjnJmRm8jg/lsXKniRSbXHb77key2tuk5nJY3gsg9U9KUf7surVc+PKPKlic/Oic8bO+eNmW4aZ29dWzQBLSnmHjfc9D2BTxb87AZyp9UsXL2ZrbRIa6XQHRkamam/YgNg2amwfNbaPMbaNml77tLc2IzudQ3Y658k+g64TFSuWlHlSzcXiQps4PX+8bssg2W0bVVDm1V//KQA3CiGa5v99C4DveLQvIiKiQHiV/2QW86TCy1GAJYRYLoR4EEAXgPuEENsBQEr5OoDPAvicEOIvAXxJSsn8KyIiqhthqRMVpYT8RuI0yf0igIfn/1f92j8A+Acn709ERBRWYakTFZWE/EbDSu5ERFQXMjN5nB2e9K3nSKsTpSeIOlHJRAzrVnYwuAoJ/hWIiChysrkCRsazSHcn0Rpvxv6DA+gfHMPYZM63RPMoFSQl//GvT0REkaE3a69tWQxDFy7PQvdzQWLWiSIjDLCIiCgytFl7mvHpWcM8KDcKbdbC/CcywhwsIiKKBNWsPT1aorkfauU/ZXMFX/PDKHgMs4mIKBJUs/b0hGFB4qALkZpRmc/G3jf3sCWJiCgStFl7ZoOsMCSa6w1p+pUfVksUgr8oYwsSEVEkqKqWr+lNoqczASA8hTbDUojUSNBV6Osde7CIiCgyVLP2OruSOD04EppE87AUItVjJvgLQxtGGVuPiIgiQzVrL9UWx7qVwQQselRDmkHnh4U5+KsXHCIkIqLIiULV8jAvxBy2KvT1iAEWERGRR8K6EHOYg796wRYkIiLySJgLkbIKvbfC8VcmIiKqY8lELHQ5TWEO/uoBW5KIiKiBhTH4qwfMwSIiIiJyGQMsIiIiIpcxwCIiIiJyGQMsIiIiIpcxwCIiIiJyGQMsIiIiIpcxwCIiIiJyGQMsIiIiIpcxwCIiIiJyGQMsIiIiIpcxwCIiIiJyGQMsIiIiIpcxwCIiIiJyWczJLwshmgH8HoA/B3C7lPLU/M9vAvBhAC8CEACOSCn/j8NjJSIiIooEpz1Y2wD8BEC26udrAHxeSvlZAH8E4DNCiBUO90VEREQUCY56sKSULwKAEKL659+s2rQAIO9kX0RERERRUTPAEkI8BWCVzksP6QRSev4zgP8upZyoteHy5UnEYi0m3jIc0umOoA8htNg2amwfNbaPMbaNGttHje1jzO22qRlgSSnvsPvmQojfApCSUj5sZvuLF6tHGsMrne7AyMhU0IcRSmwbNbaPGtvHGNtGLWztk80VMDKeRbo7iWTC0YCRK8LWPmFit21UQZlnf3EhxAcBtEspHxZCbAVwSUo54NX+iIiIwqAwV8T+gwM4cWYU49Oz6G5vxbZNK3oN41IAAAcxSURBVLB312bEWjh5v1E4+ksLIZYLIR4E0AXgPiHE9vmf/waAvwTw74UQPwTwNQBrHR4rERFR6O0/OIBnjr+B8elZAMD49CyeOf4G9h9kH0MjcZrkfhHAw/P/q/z5v6AcdBERETWMbK6AE2dGdV87cWYU2VwhFMOF5D32VRIREblkZDy70HNVbXx6FqOTMz4fEQWFARYREZFL0t1JdLe36r7W3d6KFZ1tPh8RBYUBFhERkUuSiRi2bdKvq71t0woODzYQ/qWJiIhctHfXZgDQnUVIjYMBFhERkYtiLc24985rkM0VMDo5gxWdbey5akD8ixMREXkgmYhhXYKV0xsVc7CIiIiIXMYAi4iIiMhlDLCIiIiIXMYAi4iIiMhlDLCIiIiIXMYAi4iIiMhlDLCIiIiIXMYAi4iIiMhlDLCIiIiIXNZUKpWCPgYiIiKiusIeLCIiIiKXMcAiIiIichkDLCIiIiKXMcAiIiIichkDLCIiIiKXMcAiIiIiclks6AMIKyFEM4DfA/DnAG6XUp6qeO23AfwygDkAr0op/7fO7/cA+BSAQQBXA/hTKeWbfhy734QQ3wKQqvjR9QDWSilzVdv9DMDP5v95Tkq514/jC5oQ4uMAdlb86BEp5UGd7WqeV/VICPE5AFkA0wC2AfiwlHJYZ7ufoUHOHyHErwJ4N4DzAEpSyk9UvZ4A8FkA51C+v3xKSjng+4EGQAixEcDDAI4BuALABSnlJ6u2+R0AfwBAuwf9nZRyn5/HGRQhxGFc/txzUsp3Vr3eyOfOLwH4PoBfzP+oE8BJKeXvVGzzO3Dp3GGAZWwbgJ+gfONfIIS4AsADAH5ZSlkSQjwvhHhaSvlK1e//dwDfk1L+kxDi36F8Qt/jx4EH4O+llI8DgBBiA4A/qQ6u5n1VSvlxX48sJKSUO1WvWziv6lFGSvkgAAgh/gTAxwD8F53tGuL8EUIkAfwtgOuklJeEEN8QQrxTSvn9is0+DODnUsrPCCG2Avg7ALcGcbwB6AHwj1LKfwEAIcTLQohvSSmPVm33H6WUP/P96IJ3oMZ10sjnzhSA35dSfg8AhBCfALDkYRcunTsMsAxIKV8EACFE9Ut3ADgqpdQqtD4H4F0Aqr8I7wLwyPx/HwLwmDdHGjwtuJr3XwH8tcGmtwkhPgKgA8B3pJQ/9vzgQkII8TEAlwC0APhrKWW2ahOz51Xd0YKrec0o92TpaZTz5xYAZ6WUl+b/fQjl+0llgHUXgD8FACllvxBimxCiU0o56e+h+k9K+XzVj5oBZHQ2/c9CiGEASQD/S0o55vnBhcPW+QeVNgDPSym/VfV6I587FwBowdUyAG+VUv6ZzqaunDsNHWAJIZ4CsErnpYeklN80+LWVKEfBmsn5n6m2mwSwXAgRk1IW7B5vkMy0lRCiE8C6yuHUKh+VUh6Zf0I/JoS4W0p5xqND9pWqfQA8AeBnUsqMEOKPUA5AP1C1ndnzKpJMnj/dAH4NwHsM3qZuz58qZs4Fo23q/kuykhBiN4CnpJSnq156BsC3pJQjQoh/i/I1+M4lb1CfPj1/nbQA+JEQYkpK+aOK13nulP0WgK/r/Ny1c6ehAywp5R02fu08gE0V/+4EoHeTP4/yk/b4/DYXoxpcAabb6gMAvqx4jyPz/58VQhwH8Hbot13kWDiXngbwxzo/N3teRVKt9hFCdAH4GwC/a/S0WM/nTxXt3qHpnP+Z1W3qmhDiHQDegfKQ1yJSytcq/vk0gG8KIVqklHN+HV9QKq6TOSHEsyi3UWWA1fDnzrw9AH6j+odunjucRWjdUwBuFEI0zf/7FgDfAQAhxJr5BEIA+Nb8a0D5i6C6m7auzE8KuAMVn1MI0SyEWDf/3+8UQtxZ8SubALzq71EGQwjxFxX/vBrzQUFl+0BxXtU7IcQKAI8C+GMp5WtCiPfM/7xRz5/nAKyfH8IA5u8fQoie+V5ioOL+Mp9Hc6IRhng0Qoi7UL7ffAjAaiHELZXtI4T4H0IIrQPhagCvNUJwJYS4RghR2Tt+NYAzPHcWmw/OfyylzM//25Nzp6F7sFSEEMsB3A+gC8B9QoivSSkPSylfF0J8FsDnhBBzAL5UkYj8VwD+GeVuxz8F8GkhxGYAG1FOYK5nvw7gXytyiIDybMJ9ALai/IT0cSHEDQDWAviGlPL/+n+YgSgIIT6PchtsRfm8Airap8Z5Ve++i/K9aP98zuMUgG+gQc+f+R66PwTwP4UQIyjPcvq+EOIzAMZQnp38eQCfFUI8iHKwWT3kXLeEEDcCeBzACwB+gPIM5kcB7Mbl9hkG8AUhxGsonz/1OsGo2iSAu4UQa1HumfoFyt9HnwLPnUr3YfFEmo/Cg3OnqVQq1d6KiIiIiEzjECERERGRyxhgEREREbmMARYRERGRyxhgEREREbmMARYRERGRyxhgEREREbmMARYRERGRyxhgEREREbns/wfTM1vJp3RSKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(X[:, 0], X[:, 1], s=50);\n",
    "# plt.savefig('../../images/ch13/ml_plot_01.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KMeans(n_clusters=4, random_state=0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=0, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_kmeans = model.predict(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 3, 0, 1, 3, 3, 3, 0, 2, 2], dtype=int32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_kmeans[:12]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l9VkZwVpkZARLCCHECc2mTS1w0csk4VsWHAnZCJbIK4P8foQjca3i9F3WgFROoakmx0jcRm7SIgCnzZiTGmEeu8XZnUl6wjaSWRVds+jw5XAucJArikmAJYQQYlEzLRiIasTSGjbNot1XmKze4cvRH9FJlykfMabObRSVTBhNqOwfsRPNqJQfBbPQ1NIBjGHC7iE7wYRG1lRxaCZep0HaUEhk8hfzOUxWNqSxzdGckU2FZXWlgzXTyo/EhZIapgkeh8nS2ixuuwRg800CLCGEEItW1hhLYJ+YBuyL2FjdmKG5Jj+iZLflt7rpjjjHa2EpWEc3vVEAi1qnweqGiQT3nAk7BhwEE8efXvTYyxcf3T1kZyA2sYdh2lBJJxWW+LMEGjNoqoXHbs3L9J1l5d/TcHzi1h7NaERSKhva0lW//nBcYyimYVr5nLDO2txxa4BVQySlMBy3oSgWrd6Fm8KWAEsIIcSitX8kv4nzsTKGysGgTqNnou5TU43B6csUdhxMYpj5fwcTGsmsQo3DosFdWEJi37C97JTgsRyayYoy+U3pHIwkSiXXK4wkNFY0ZOe1LlUwoTIcL25PIqvRHdJZ2l69a+8b0TkS0rGOBqtD8XzfbGhLo1dnm8oilgV7hnX6Izrm0Xb0hE2W1WVZMsVFCnNJAiwhhBCLVrjMPn2JrMZgVKPVN5EXpaoKLcfkSbWUyZkyLRhNHn9oxakZnNOZGt9UurgNatmtedI5FcMEdZ6CC4BQsvxoXLyKm0bHMwq94YngakwkbePwqMmqxvkp39Ef1eiNTOyRCfnyHAdH7dS5DGrmuZK/BFhCCCGqaiyHKnK0iGa7LzflnCCrQv66OcP7pWXlVxQeT5s/Vza4AvDoJrpqki0RZDl0czznyjDzeVHRlApKfmucpXW5OR/dKpcnBsxZ/lcpgzGtZGV/gEhaA+YnwCo33WuYCgNRGzWO+a3TJgGWEEKIqjFM2NbvKBhd6Y/aWFGfrbiqzrSgN2yjXHxl10yaamZWlFNV8hXpI6lyz7BodOeOO61kt+U3ou6LTo5eLJo9+U2vDRPe6HMUTHOOJm1EUhrr29JzGmS1+3L0lix0amFTTSyrOiM4ld/C/I0alQvyAMwKx6pF6mAJIYSYEcvK/1fJwaBOKGlj8rTNoVGdbJn4xbRgW5+DvSMO0rniOTYFiw5/dsa5PYoCHb4sNmVy4y3GAoJkbiKfyTChe9TG/hGdwZhW8J7XNGXo8GVw2gxULNy6wbK67Pgqv56wrSiHDCCY1BiIzu38od0Gqxoy2LXJgafCQEznlf3VCXZavAY2tXQoPJ/FT2vs5a6V395pvskIlhBCiGkJJxUOh3SiaQ1Vsah1maysz5ScTguVqbCeMVT6Y7aSo0TdozZGk6VuTxZ1rhwdfoNGz+xumC1eA01N0xfRiGdUUrnCMg3xjMaeYQXTTHMoZCeZHXsf+RWJZ7Tmk7dVBdY0ZVlpZsmZCrpWWGsrWiaHDBTCKY0239ze+Fu8BomswqHRyf2ucGAIvLa536/QpVssqc1yaNReMFJU58qxrG7+puWW1GYZTWpF+zU2emb/+zITEmAJIYSYslhaYceAs6DmVH80v1rvrPZ08Wq7CoMm5Ua/wmU2NAaFGrs1ZzfLsRvvniE7PZHia2YNlX0jjkk5VgqhlI39IxaB5omyD5paOgeq0hRgtVYYxsr0n0V+ZV81NoReVpej1mUwGNUxLfA6DVq9xryuotQ12Nia4nBIJ5JWURWodRosqcstSJV7CbCEEEJMWU+Zgp7hlMZATKN10so9r9Mkmim+4dvUmeVQVWOSK1uhGdkyyfCh1NQybBo9BgOxwilSABWLlppqje4sTN0nv9PC76y8mXa16TbmbdXi8UgOlhBCiClLlt3MWCFWYjpsaV0Gj92Y9Mz8SsJyBSBrnaUjHlWxaK6Z+3pGlbeZKf1+TUs5bv4Z5OtxdfqzqMfke2mKxZK6DH7X1AMhw6TiPorHqnOVfqKmQoM7R9Y4fu6cmD0ZwRJCCDFleoVSAP1RDa/DLKg/5bTBWW0pusM68YyKplo0eYyi0at4RiGdU/A58xXAQ0mN4DF5WAoW7f4sPufcRwad/izDcY1EtnCkzWUzMC2l5Ihdjd2Y8rTT6sYsLd4cQzEbCtDinXqZilRWYe+InUhKPaZCepZGT/loq92fI5TSjibp5xupYOG2K+wcdGKYCh6HSYcvW7ZWmJg9CbCEEEJMWbPXYDhuKyoqCZAzNXYNqmSNDJ3HJK/rNljZUHraJpVV2D1kJ5TSMC0Fh82kpSbH+tY0A7Ec4ZSGAjTW5GioQu4Q5FffrWtJc3BUJ5rSsACf02BFXZbhhMbBoL3g/do1kyW1E+/HsjhusOV1WHinWYfJtGD7gKMgaTuUUkkMqdjVFL4yI2CqAme0pBmMaYSS+f4LpxSiKRtjE1eRlEYirWJTUzRUCNYmCyUVBqI6WRPcukWnP1uxVtipTLpFCCHElDV5DPxOg1CJ0gMAFgr9URsd/uMnFlsW7Bq0F5wrnVM5HNLRNYsltbk5X2VXTo3DYn1rBtPKj/mMtd3jyOG0WQzFNLKGimFZOGwWwYSNVNZgMKYTy6jY1PxqylUNmTnbf68/WrwiDvIrMHsjOj5X+XwnRcmvKGzxGgzHNHqjjqLn5CyFvqiNBs/U8qaOhGwcCNoL6k0NJzTWt6RlM+kSJAdLCCHEtBzvZhrPqKRzx58/G02qZco4KAzFF+b7v6oUj0a1eA1WNmTJWRDL2BhJ6BwO2dk15CSYtJExVBLZ/DYt2/uLA5mZSmbL36JTxtSXxUUzCuVyydIVrnEsw4TusK2omGcio3FoVC/zqlObBFhCCCGKWFb+ploqGbrGUXlUyaZa2LTjj2gkMoW1p45VrgjpQjkQtBMvWg1Z3PbRpMZoYm5urfYKfVjp2GT5xQSln2+3Te08gzGtZNFXgMgUV1SeamSKUAghxDjTgv0jOiNxjayp4NItWr25gm1tWr0GA9FcyQrlkF/FNpW97/xOA1WxSm5jUnll38yM7YmYMxUaPUbZVYyTjSZURuJTCyIsFMLpuSnm2e7L0RexFSXfa4pFyzRKXLTUGPSEzaLpRoW5WZW5EDWmTgQSYAkhhBjXNWhnIDYx5RNNQyytHl3Fl7+pqwqsb02zd9hiOD620a9CvtK6wZrG9JSu5XVa1LvzSfPHUpV8UDeXhuMq+0fs48HKoVGTVm+OVQ3ZigHCgaCNw6P2kkn95TinOCp0PJoKgeY0+0fsRFIaFgpu3aDDn6NhGsVWFQVOb05zOOJmKGxhouDSDdp9uSmvImyuMTg8apAsMYrlm8ftcE4kEmAJIYQA8jWuhhPFN1ALhf6YPh5gQb5q9tqWDJaVz6WKpVW8DnPaIzdrm/OBWiipkjUV3LpF2wzLB1iWRSqnoClWwT6FORP2DttJHRMc5EyVI2Edl27S4S99rURG4UhoesGVx27QPMNNqEvxOy3O7kgTSysYpoLXac6oOrrHbnH5WpXDPTEyxvTPo6mwrD7LvmGloLK912GwvH5hi4suVhJgCSGEACCcVDHM0lNhqaxSshyBokC926R+hlNi+VGa/Oo90wKtRJL5VPRHNd7otxiNu9BUC7/TZHVjBpdu0Re2FQRXx7SekYStbIA1EC1O6q5ExWJVfboq28PUOMrnUU2Hy27hmuF5Wr0GPkeK3oiNnKng0U3a/bk5WzV5spEASwghBAAeu4lKfgppMrtmFQQ+4eTESr9Gj0FtmerhU6UqM9+bbySusnfIQc4CUMiZCiMJleyAwtkdqbLb3QDkKhyb7jsyUdg97MARsvA6TJbWnnw1otx2i9WLZCuaxa7qP/pAIHAN8G5gELC6urq+Wu1rCiGEmD6v06LWZRRUUM+zaPRM5ETtGdbpDevjU2c94fy03prGyvlM1dIftZErMdIUSasMxDRqXQaHQxalVv259fJhVJMnR3dIL/m6clI5jVQOwikIJTU2tqVOuiBLTE1VB/YCgYAb+A7wua6urq8AGwOBwNXVvKYQQoiZO705TYM7h3Z07zy7ZtLpz7KsLh9gjcRVeo4JriCfo9Ubya88XAjpXLlbmUIio1LnMmlwF08DOm0GS/zlR2N8TguXbeYjc7GMdjRAE6eiasfVFwGHurq6xpaUbAHeDjxR5esKIYSYAbsNNrSlSWQUklkFn8NEP+ZOkZ8WLDWik0+Qb5xmgnfWgHQuXw5iprk8dpsJJSqeA7h0E0WBM1rTHAiahJIapgkeR367G4+jcj7ShrY0r/c6SBvHnt/kaL3347YtlpEEpVNVtQOsZiB6zL8jRx8rqa7Ojc22MN+AZqKpybvQTVi0pG8qk/6pTPqnvIXumwNhs/BT/Rh2h05T09QqmecMi1cOWPSHIZ0FjwM662HDUgVlmvOMp9tMnt8LuUmxXZ0H1q90oR49X0vR3WdqbW1uNNm8E6KpsUemHjSpmo2mJvuUn19tC/37s5jNdd9UO8AaBI5tse/oYyWNjiaq3Jy509TkZWiozKfMKU76pjLpn8qkf8pbDH3jVDTAWfoYKYaGpjaCtWPAzuAx9bbiaejqs0ilMqyon14StQ1YVa8xEHcSSlhoCtQ6DVbVZxgZnv3Ku12DdqKpmU31heIWOw4kaJrD0g0ztRh+fxarmfZNpaCs2gHWVmBZIBBwHJ0mvAT4VpWvKYQQi1K6Z4CB7/+U1KEe9PpaGm97B97zNi50s6al1WswHM8xkii8fdS7crROcWPmVFYhWKLeVn4PQo3lddNPlm/zGWxYqXC4L4GmgGOOin2aFoSSM5/mMyyF7rBOo8eQiuenmKoGWF1dXYlAIHAX8M1AIDAEvNHV1SX5V7NgGBYPPTbItl1RDNNi9XI373pbC26XLFMRYjGLb9vF3ju/RPpA9/hjwYefYMmXP0vz+9+5gC2bnrF8pp6QQSiV30vQ7zTorM1NucxCPKOQK1NvK5NTxuthTb9t+UKlc8m0KpdyKFR6pWI8rZI1kNWEp5iq/7i7uroeAx6r9nXmk2VZPPfiCK+8PkJDnc4VFzdgs1X/q4llWfzf7x5g60uh8cdeezPK9q4Yf/E/VuN0nDj5a0Kcanq+8f2C4ArACEfp/+5/0PieG1HtJ85qM1WBJXU5lszw9TUOE101CyqCj3HYrFkX6rSOFi1VZ1i09Fiakq/9FEkd75nlAztVmf17EiceiaenKZHI8XffPpAfQTo6Gv7wE0N8+iPLWLXcXdVrP/9KiOdfDhU9vnNPnAcfHeQPbm6r6vWFEDNjmSbx13eWPJbac4Dw5uepu/ayeW7VwnHYoN5tMBCbHGDlNx+eTVDUG9boj+okswo2Nb/X4arG7IwDHEWBDl+WeFqdVNXdosZuoCoK2tHrjMQ1QiU2wPY5TU6g9VtijkiANU0/uq+H17YXJsId7E7yg//s5utfPG3aq1+mY9vOKFaZL0l7D504CwSEOBUptvJ5PKp98awymy+B5gyqAsGkRian4NJNmmpyLK2d+SbPBwdN9g47xivRZ03oiWjkTIW1LTPfL6/Fa6ApaXqjNtJZBV3LB4Ltk7bYqXUZ7BpUiGcmoqkah8GqBtmr71QkAdY0WJbFtl2lVxns3h+na1+c01fXVO36eoVpSH0mCQtCiHmhqCo1524k2N1XdMy9/jR8l563AK1aWKqSD7IME7Kmgl2b/TTawWFKbvMzHLeRyGZnlZ/VWGMct8aX12FxTkeKvoiN1NGgsc1nyPTgKUoCrGkwTUinS1f1NQwYDVd3f6a3XlTPo0+PkEoVtkFR4JyNvqpeWwgxO51f+m+k9h4k8WbX+GN6azMdn78TRTt15480FTR1bhLT42XypAxLIZRUceuzL5WQzCocHtWJpVVUFfxOg+X1E1OQmgqdsxiFEycPCbCmQdMUlna6GA0Xj2I1N+qcdUZ1g5yVyzzcemMLv35kkHgy/0Gh6wpXXlzPlRc3VPXa1RKN5egfStPR6sTtOnVvMuLk51zSztpf/4DBe35Oav9hbPV+Wj78XuxtZWsvi2ly6JAoMRunYOGxz24zaoBUDrb1OUhkJz6rwimNeEZlfWu6bO6YYcLhkE7kaLkHr9PAtBRi6fy/fU6TZXXZGVeyF4uTBFgSpyc5AAAgAElEQVTTdPO1zRzsThCOTHwT0m1wzWWNuJzlA4TB4TS/emSAg91J7LrKxnVebrmhBW2aU3vvuamNt5xby+bnguRMiwvOqmXtmupNS1ZLJmvynR8f5pVtEcKRHPW1Ohee4+dj71sy7T4R4kShuZ203fXBhW7GSau9DkbjxaUSal0GfufsR8m6R/WC4GrMSEJjJF56myDTgm39DkLHbKA9OikRPpSCSEplY3taphNPIhJgTdO5G/386V2r2PTcKId74vhqbFxyfh1XXlJ+BGk4mOHr/7SPwz0T49dv7IxysDvBn9y5ctpt6Gxz8YFbO2bU/sXiuz8+zJNbguP/DoayPLJpGF1X+cgfdi5gy4QQJ6q1HQrhaIbBmI2MoaIqFrUug9Oa0sd/8RQksuU3lQ6l1JIBVm/YVhBclRNK2egNGzK9eBKRAGsG1gVqeOulbVMuq/+rR/oLgqsxL7waYtvOCBvWTn1q0bIsHt08zIuvhUkkDZa0O7n5uhY620pvXTEVTz8f5KnngoyMZqiv07nyogYuv6h+xuc7nmgsx8vbIiWPvfhamNtvbUevsOJKCCFKURSF1Y1ZltVmCadVXLqFxz53hUdVpfy5yuWRRdJT/yyLTuO502VZsKffpHvQgWFBjd2kszaLU6KAqpGunQeHj5TOvMxm4fUd0WkFWD/8zyM8/MTQeLmGnXvibNsZ408/vYLlncevw3WoJ0EyabJmhQdNU3hk0yA/uq+HTCZ/wsM9KXbsjhGJ57jpmurkhvQPpglHSn9LGw1liccNav0SYAkhZka3QaNt9jlXkzV6jKNbBBXO4+mqSbuv9GfadKb8qjk92DVkpz8KY7f9UBJGkxob2lISZFWJdOs8cDjKBwuVjk02MJTmqeeCRbWw+gbT3P/IIJ/5xPKyr91zIMY9P+uha1+cnAFLO5y87apGHt08PB5cjclkLJ54epi3XdlUlXyojjYn9bU6wVDxqsumBjveGvm1FGK2LMvCSmdQHPaq1uc7lbR6DeKZLH0RfbzoqMNmsqI+g6PMx1aTJ8dA1IZVonzEsZSjRVarIZxSGIwWNzCe0ege1VnTVN0V8KcquZPNg7PX+3jljUjRRgr1tTrXXd445fNsfTlELFF6mfHBI8myr0unTb75g8Mc6Z0YSTvck+Ke+3pJZ0p/yzvcm2JwJENbs6PseS3LIhY3cDhU7PrUA0W3S+PCc/w8smm44HEFuPi8WklyF2IWLMui9x++R/CRJ8kOjmBvb6Hhlutp/aMPSKA1S4oCqxuztPtyDMU1VAXafDkqZTQ0ePJTcb3hiaBMOXo3GAu6NMWiszZLnXvuR90AgnFbyfpgALGMBkiAVQ0SYM2DG69u4tCRJM+8ECSVzv9hNdTp3H5rO37f1Pcf81QoY2DXy39wPrp5qCC4GpNKm9hskCvxpcnt0vC4y1/vqS0jPPLkEEf60rjdKhsCXj72/k487olfqeGRDI9sGiKWyNHe6uSGK5uw6wpDIxnec1Mruq7y4mthgqEszQ12Lj6vlj98p2z3I8RsdH/tn+n/9o/H/50bDpLYsRsrl6P90x9euIadRNx2i2X2qY82rWrI0lKTYzBmQwGaavIbYw/E8p+XLTU53HOYKzZZpdyxSsfE7EiANQ8UReFTH17G9Vc28tJrEex2lWsvr6fGM73NXd96cT2//t0Avf3FK2LWn+4t+7qR0fLfTjwuG+Fo8QfFhrVefGWm6l54ZZTv/bSbRDL/bSuRNHjyuSDhaJYvf24NAM+/PMr3fnKkYBrwkSeGcDlVjvSnsOsa607zcPd/W4Hfq+OtscnIlRCzZCSSBB96vPhAzmDkV7+l7c7bUWzysb8QahwWNY7Cz+IV9fMzctTmy3EkrJfcXLvONfviq6I0+UubR6uWeVi1zDOrc6xbU8NoKEvyaDV3m5YvHXHbLRMjP6Fwlk1bRjBMi8svrKejrfw034a1NUSiBtt35zev1jRYd1oNn3j/krKveezpkfHg6ljbdkZ5c1eUy+pruPf+vqIcq4HhiQqAuZzBS69HGA1l+Zs/O12CKyHmQGr/YTLdvSWPpQ8dIRsMYW+eelqCODnYbbCiPsOhsJP00Y9lBYtGT44lUhaiaiTAWgDptMmmLSPE4jnOO9PPiqXHX/3X05/i/37nAAcOT+Ra1XhU/vDmVt5+bct4bsVDjw3yy4f7GT26Su+B3w5y7VsbOG2lm937CzeErvPbeMd1Laxe4ea17REOHE6yYomLs9b7KuZqDI2U3rg0m4M9BxLo9hEOlVk5Odm+Q0k2bRnmurc2Ten5i8Ujm4bY8vtRRsMZGurtXHlxQ8VaaELMB3tbC1p9LUYwVHRMb6jH5is/0i0Wr0hKIZTUsNssmmtmtrdhu9/gtKXw5oE0hqlQ7zaqlvMl8iTAmmcvvhbmRz/rpncgH6T88jcDXHJ+HZ/68FLUCn81//7znoLgCiAWN3nh1Qg3XdcKwMHuJPfe30f8mET4WMLgoccG+cQHltDUYGfHnjjZrMnKZS7ecV0La1bmR9TOXu/n7PX+Kb2HWr+Nwz3Fj6sqdLY5yOam90fbPzi7IoC5nMV9D/byxo4oqbTFsk4n77y+hZXLjh+4zsTPH+rjZ/f3YRzt5t6BDF374qTTJjdcdWIFiuLkojfU4n/rWwj+6rdFx3xXXozqLD+aLRYf04KdAw5G4tp4knp3KF84dSaV6V12leX1MmI1XyTAmkfptMkP7+2mf3BiBCiVNnni2RE62hy8622tJV+XSBrs3BMreWzXvhi9AynaW5xs2jJcEFyNyeZgx54Yn79rJemMiWFYs9r379IL6tnRFSM36VKnr/Jw3pl+/LU1tLXY6RsoPdI1WWuFlYpT8Y/fO8CWFye+sR86kqRrX5wv/veVU6oNNh2ZrMlTW0bGg6vxxzMWjz8zzHVXNFYMlIWotuV/+yXI5Qhvfh4jEkOrr6Xu6ktY9tX/sdBNE9N0IKgzFC+8TcczGnuHHZzTkSq796FYHCTAmkePPzNcEFwd69VtkbIBVs6wioKZMUYuH7gBpNPlv9GkjuZsOeyzL+B57eWNRKI5nnh2hL6BNA6HyvrTavjE7UtQFAWHXeWW61u45+c9BblaikJRDa9Vy1xcdcnMc0J27I7y+9eKp0MGhjI89Oggn/7o8hmfu5Qjvanx0ceiY/1pwtEcdf7pLV4QYi7Zajys/u7fkDrUQ3LnHjxnrpMNpU9Qo4nSX4SjaZWRhEqjR6b4FjMJsOZIJJZjaDhDe6uj7KbP8WT51RrJdPk/FF+NjZVLXbzZVTyKtWKZi2WdLgDWrHTz6ObS51i+xFWh9dN369tbufm6ZvYfTFBbq9PaVDgKdd0VTSzpcLLp2SCxuEFLs52mejubtwbZfziB3a6ybk0NH7mtE5tt5l/Dtu2MkS2zEKe7RGmK2aqv1XG7NBIlfpY1bg13hQ2/xcnHMgyCDz9BpmcA32UX4FkfWOgmjXMu68C57MTes/RUVz7bQiGdk+GrxU4CrFlKpQ2+fc9hXnszQiRm0NSgc/H5ddzxno6iqaKz1/v45cP9pDPFI01L2yvvJfiuG1vo6U8xGp6YP6/xaNx8XfP4da68uIFnXxjl9R2FeySuXu7mHde1zPQtlmXXVU5fU1P2+No1XtauKUyqvfHqJoZGMtjtKrXTqAFWTo2nfEDjmsU0aDm1fp0Na2t44ZVw0bGN67zTqswvTmyxN3Zx6O6vE399JwCq20XtdZez8p++iqrLR6uYPY/dJJUr/hzTVZNGt5RXWOy0r3zlKwvdhnGJROYrC92GqfJ4HCQSGf7x+wd59oXQeNCUSJp07Y0DsGFSbaqGOjs9femiquutzXY+/oHOikVH21qcbFjnxTItan021q7x8qH3tnP+mbXjz1FVhYvOrcMwTRQlf723nFPLnR9aOqvtZ/bsj/Pgo4O8+mYEm6bQ0lQ5Z2qsb0pRFAWP24bTMTfBz7IOF1tezI+SFVwHePs1zZy2anZlMUpZH/ByqDvJyGgG08wXeT1no59PfXgp+hQq2lfqH3Fi9I9lWez9+BeIv7Zj4rFsjuSufZiZDP7LL6zKdU+EvllIJ1v/6JpFMKlhWsd+Wbdo8+Zo9k5/evBk65+5NNO+8XgcXy13TL5mzcLgSJrX3oyWPPbCyyFue2dbUbmDW29qoW8oRd9ABlWBtQEPf3hzG0s7jp+MvXKpm099eFnF5zgcKnf8QefU38Rx/PjnR/jNE0PjAeQjmwa58pIG7rxj6aLYdsPhUPnY+5fwo5/1cKQvPyXodmtc/pY63n7N1Ff0maZFJJrD5dSOOwpV69f5y8+vYXtXlAOHEwRW1rCmCoGcWLxCm54rCK6OFXnmhXluTXVZlkXwN5sIP7EFDAPvpRfQeOvbUFQZra22erfJupYUPWGdZFbFplo0egw6/bIS8EQgAdYsHD6SKrlqDyAYzpLJWDgcE0HIS6+H+dY9hxk9pgDn/oPJkkU7F4NtO6M89PgQ2ezElGY2ly80uv50L5ddWL+ArZtw7kY/G9d62fx8fiTrwrP9tLVUnnI91qNPDfHo0yP09KXwuDU2nO7lEx/oxO0u/vN44ZVRHnt6hMHhDD6vjYvOrWX1yuqUgxCLV6anD8zSf7e5SOkVvyciy7I4+IWvMfSzB8HIv9/hnz9M6LGnWf3t/4WiSc5htdW5LOpcMup0IpKvILOwapkLX03pD5imBjt2+0RwZVkWP3+wryC4gvxqt/se7K9qO2dq60ujBcHVGMuCl18vzkGaCsO0sCYvJZwDuq5yzWWN3HJDy7SCq6e3BvnBvUfYdzBBKm0yMprlqa1BvvG9g0XPfeaFIP/0g0O8/EaE7t4U27ti/PDeI/zkl6UrZ4uTV921l6M11JY85j5t5Ty3pnpCTzzL0H0PjQdXY0YfeoKhex9YoFYJcWKQAGsW6mrtnHdWcXFOVYVLL6grmELr7kux92Ci6LkA23ZGeO7FYNXaOVWZrMmjm4f51SMD9A2kyBnlA6FKx0rZtjPK176xl098fht3fXE73/z+QcKRhd/BfdNzI2RKLDp4fUeUXZNqj/3uyWGSk0YbTRM2Px8kWWGFqDj52NuaaXzn9UwuRGRrqKf5I+9doFbNvfDjz1KuRkzk2RfnuTVCnFhkinCW7rxjKQ67yivbIoQiWVoaHVx+YR233FC4ak85+l8phgHf+fFhXE6NszdMrZr6XPv9qyHu+XnP+EbSv/hNP2tWlJ/6CqyqobsnSd9gmnWneSpuXL3/UIJ/+v7Bgk2nB4aCDAyn+as/PQ1tAQtzDgfLbPuTtdh9ID6+SjKdMcdzvIrOMZJl++4Y5525MD87sTCW/vXn0TtaCD36DEY4gmPFUlo+8gf4L71goZsmhFgEJMCaJd2m8snbl5LNmsQSBr4aW8mNizvbnKxZ6WHX0RWGk0XjJr/ZNLQgAVYyafDDe48wMDQRbMTjBq9vj7J8iZOD3YWBxelr3LzyZpj/+EUPmaxFfa3OZRfW8aH3dpRMfP/NE4MFwdWYnbvjPPvCKG+9aOFyuer8Oj19xVv12DTG64sB2GwKbpdKuMSaBruu0NggxUVPNYqi0H7XHbTfdcdCN6Vq/FdfwuBPf11yFMt36fkL0CIhThwyRThHdF2lzq+XDK5M02Lz1iB2u4KtQk7o/oNxHnpsgP2HSgdh1fLo5uGC4GqMZeUDkI/e1skFZ/s5/yw/739XGzZN5bU3o2SO5mcFQ1ke+N0gv3pkoOT5B4ZLjxJZwMHu0tOm8+XSC+pK/kzWnlbDxrUTZTY0VWHjOl/Jc6w9rWbOt+QRYjGoveYymt57Uz7v4Rh1b7+KptvesUCtEuLEICNYVWaaFv/wrwfY8vvi7VwmC4YNfvCfPTgdCuds8POZTyzHPoW6SrMVK7MSEvJ7Jd58XTM3X5ffamN7V5T7Hugrep4FvPBKiHffWLzdT6UaXHW1Czvyc/0VTcTiBpu2jNDbn8btUtlwupdPfnBJ0WjcR27rJBzJ8sq2CJmshapCYJWHP/rgkgVqvRDVpSgKy//uz/FdcRHhTc/lyzRccn6+TIOsIBSioqoFWIFA4BtAAogBZwKf7erqWpzL5arooccHphRcHSuVtnjupRB+3xE+efvSOW2PYVo8vXWE3fsTOJ0q117WyPrTa7j/t/kSDJN1Tqowf6gnWXZfxHCkdG2Wyy+s5+U3wkXJ5B2tDq576/H3ITRNiwcfG+T17RGyWYtlS1y8+8YW6mvtx33tVIxt+9Pdk6LWb6OhrvR5HXaVuz+9ij374uzYE6O91cF5Z/oXRT0wcXyWZRHetIXoi69jq/XRfPu70WqkftnxKIpCw03X0HDTNQvdFCFOKNUcwYp3dXX9OUAgELgb+DPgv1fxeovOLx7u52f3F4/2TNXrO6KYplW05c5MpdMm//tf9vH69olEoieeHuH2W9s578xatr5cGAi2NTt4x7WFyfob13pxOVWSqeIaQC1NpQOTt5xby/uH2/ntpmH6h9Koan77ng+9t3NKFd3/+YeHeOq5iVWWb3bF2N4V48ufWzVnQZZdV1m1fGrTfGtWeaSw6AnGTKXZ+8m7CT21dTyfaOCe/2L51/+U2qsuWeDWCSFORlULsMaCq6NU8iNZJ4zDRxL8+L96GQlm6WhzcMsNLaxeMfWb6us7Itz3QB/ZXPlyBmet89LUqPPY06VLNMTjBjnDwj5HAda99/cWBFcA0bjBfQ/28/d/cRodbQ627YySzlgsX+Lilhuai0awOttcnHemn2deGC143OlQufry8qNR77y+hRuubOK17RFq3BrrTquZ0sjPjt1RtpQoYXGwO8mvHhngY++T6TlxfEf+9luEHn+24LHMoR4O//U38V16AapdFikIIebWrAKsQCDwO6DULsJ/0dXV9cDR59QC1wG3Hu98dXVubJWywOfJD356gB/fdxjj6FTYwSNJXnglxMdvX87t75nYqqapyVvmDPDS673jSeClqCr8wS1LOHdjHa/veInB4eKVbMuXuOlon7tVhXsPli4zMDKa5eU3k3z2j06f0nm+evd6vvVv+/n9q0Gi0Ryd7S7eeUMbN1w1kX9Vrm86O6b3fnY9MkS2TLms3v5MxZ/BYnaitnu+zHX/dL34WsnHU137yD75NJ233zLraxipNHv/9ruMbnk5v2fo+RtY/aU70X1z+14q9U3wuVcYfPhJVLtO54fejXv53G2bdaKQv63KpH/Km+u+mVWA1dXVdX2l44FAwA98C/hoV1fXcStpjo4u7IoygL0H4gXB1ZicAT+69zDnrK+hqcFOU5OXoaHS+xACjIZKBzNjrri4gbWrHCQTSS4+38/9vx3k2ALnbpfKVZfUV7zGdKXS5fevCoVSJa+1a0+MLS+NYhpw7pk+zl7vQ1EUPvCuFt73zub8CNvRRPyx1x+vb6YjVyoxbIxizWn/zJe57J+TUTX6Jx0t/9kyemQIxyyvZ+VydN3xOSJPbR1/LLj5BQaffonAT/8FzT313QUqKdc3lmVx8PNfY/iXj2Cl86t293/rJ3R85qO0fuL9Jc8VfHgTIw88ihGJ4Vy5lNZPfgDnso45aedCkb+tyqR/yptp31QKyqqZ5N4I/CPwha6urp5AIHBrV1fXL6p1vbmy+flgUXA1JpU2eXLLCO99R9txz7O0w8WWF0snt191aT2f/sjEZsl3vKeDOp/OC6+GCEVytDQ6uObyBi4+r27G76OUVcvc7D1QfKPxejQuf0txLap7fn6E3zw+ND4S97vNQ7z1LQ18+qP5tquqMmfTl+Vce3kDv900xGiJBPqz1sk3sVNRumeAvn/+N+Lbu1DsOr6LzqX9jz9acZrPvW4NqT0Hih63NdRR//arZt2mofseLgiuxsR+/xoDP7yX9k9/eNbXqGTwP36Z37rmmG9pRjBEzze+j//qS3GtLFws0/ON79P7Tz/EyuSDscjm5wlv3sqaf/0/uNetqWpbhThVVLMGwKPARuAngUDgKeDDVbzWnMlVyJmC/Iq2qbj5umZWl0iaXrvGw50fXFqQf6QoCu+4voWvfzHAv3x9HWdv8PHgo4Pcdfeb/MX/2c2mZ4en9ybK+IObWlmx1FXwmE2D665opKmhMFl8e1eUh48JriBfcf7JLSM8+dzInLRnKupq7bz/3W3U+ia+C+i6whUX1/P2a5rnrR1iccgMDrP7Q59l8Mf/RfzlbcS2vkLvP3yPvX/0xYp7XLbedQeOpZNGZxSF+puuxt46+9+j2CtvlD2W2LZr1uc/nshTWwuCqzFGKMLwzwr3DMyOhhn891+MB1dj0vu76f1/91S1nUKcSqqZ5H5Otc5dTetOq+G3T5YOaHSbwiUXTG1UyeXU+LPPruK++/vYfSCOqigEVnm47ZY29Aq1rX52fx8/f6gf8+givf6hDF3742QyFjdc1TTt93Oshno7X/38ah54dJBDR1K4HCoXnVfLW84tfk9bXwqV3ugZeG1blKsuKZ3QfuBQgn/9jx72HYzicWucf5afG65smlUpg2sub+LcM/08/vQImYzJORv9rD26hY1YPCzTJNPTj+p2o5fZCHm2+r/97yR37Cl6PPT4M4QefYa66y8v+bqajaez+p5/YN/H7yZ14BCYFlgWwYc34Tp9NS0fes+s2qU6y08BKhWOzRUzVX5fTzNdGEgFH3iMbP9Qyecm3tg5p+0S4lQmhUYnueT8OjY/N8LL24rnYm+4spEl7a6ixw92J3ns6WGisRxtzQ5uurYJb41OrU/nkx+cGJofGknz01/1cvhICodD5ZwNPt521UTwkU6bbH4+OB5cjclkLB5/doTrrmicdckGb43OB959/DyLyhs9F5doANizP8bfffsAQyMTH/avbY/SN5jho7cVJtsapsUTTw+zvSuGosKZ67y89aKGsu+vzm/nD24+/tSsWBjD//Uw/d+/l8SOPWgeF94Lz2bpV/9kznN6Erv2lT5gmES2vlw2wIL8dF1q/6GCkZ7ccJCev/sutVdfjKOzveD5kRdfZ+Bff0Ji1z40jwvfpRfQ+ad3lZyKbHz3DQz/7AHMeLLwgF2n/sbCKUjLshj4t/sIPvg42aFhHO2tNL73Zhrfc2PReTN9gwz88Gdkh4LYO1rx3f0xSn1su9efRvjJLcVvWrfhv+zCgocq1f5S7HNT9kQIIQFWEVVVOGu9nz0HE0Si+WQsp13hve9s5V1vK77BP7V1hH/7zyNEYhOJW1tfDvGFT60oCMYGh9N8/Z/2cbhnIvn9lTciHDqS4q4P5YOwg0cS9A+W3lamtz9FNJbD75uf5eRnnuHl0c3DpWYdxjdAnuz+3w0WBFeQv5dt3jrCO65rprE+/+FtmBZ//60DPP/KRI7a5q2jvL4jxmc+vkwKd55gQk9u5dCf/z1GJP+lxAhHCT36NNmhIOvu/z6Kbe4+ZjR38Rec8WOe8seAfCXyEr/QueAogz/5NUvu/tT4Y7HXtrP/zi+R6Rscfyzxxi7Sh3pY872/LTpHzTkbaP/jj9L37X/HCEXy7fHW0PTBdxcFfb3f+D49//j98Xpc6f3dRF/ehpFM0vLBicXWoadf4OCf/DWZnon6zOGHHmfZN/+KmjPXFpyz9VN3EHn298Rf3V7weP2NV+K/6uLCx26+ht5//jdSu/cXvQ/vhWcVPXY8mcFhjGgc5/JOqe4uxDFkL8JJtr48yn/8omc8uAJIZSyeeymMMWlUJ5sz+cXDAwXBFUB3b2q8wGgyaXDfA3385d/vKQiuID/d9vTzQQ4czieeN9TZcbtK/0hqPBou1/x9eL3lnFrOXFccSJ1xmoe3XVl6qvLgkWTJxyNRg+dfnqib9cTTwwXB1Zinnw8WFTsVi9/QvfePB1fHir/6JiO//t2cXqv2mstAK/4bsTXW0/yBd1V8rZkqLoUyxpo0jdb//f8sCK7GhB5/hsjv8yUf0n2DHPzy37Pz3Z+k6/2fRrHZWPebe2j//CdpuO0dNP7hTbjWrMA8ZiWsmUwx/F8PF22ebCVTDP3011hHh68ty6L3779bEFwBxPccoOfvvlPULt3v5bT/+Catd92B99Lz8V91MUu+/BlW/cvXir6wqHadJV/8FPaOY7a1UhS8l5xH55f+W9k+mix1oJuuOz7LG5fdyrbL38P2G+5g8Ce/mvLrhTjZyQjWJE89FySdKf6Wu/dAgmdeCHLFxQ3jj726LcKR3tLlGPbsTzAymuZ/f3M/+w6VDjwgvzLx96+GWbHUTWO9nQ2ne3nh1XDR885c55uXfQnHDI1k6B8qzuvIZC0oM8DkdJRv39jI2669Me57sPSOSZYFr70ZmfPVk1MRTxhs3ppP3r/i4gbc8xjMnujK5fMAJPcdmtNrNd52M/HtXQz//GHMaL52sd7WTMfn/wh7e6mSfBPc69YQ2fx88QHdhu/S8wseSpVpt5XOEH3uJZzLOtjzwc+S2LF7/Fj4qeeJbduFYhiMPv4sVjL/2dD/rz9l2d98Ed/5Z5LYtZf0wSMlz53af5jcaBi9oY7U3oPEXtte8nmxV98kF4lh8xV+AdLr/Cz98h+Xff/HqrvhCmouOIvBe/6LXDiCZ+NaGm65HkWd2meMZRjs+/SXib/65vhjie1dHP7qN9Cb6qm77q1TOo8QJzMJsCYJhcsni/YNlv8GXESB+x7orxhcjXHYJz7U7rxjKenMQbbvjpLN5oOWs87w8vH35yuWP/NCkN+/EiaVMVjaka+27q2Z+2nDBx8dpL/E+91zIMHjTw/ztquKV15tXOtj38Hi97us08nF59VxsDvB//3OAUZGy/fxQswOPvTYIL/+3QAjwXy7fvXIAO96Wws3Xi2rFKdCby2/+MK5clnZYzOhKArLv/YFmj/4bkZ/8ySq00Hjbe9Arzt+Adu2T91B5NkXi1b11V1/Bf4rC6fRbH5f2fPoTQ30/b8fFwRXY0YfeIzJSZTJnXs4/OW/44yH7y5J7S8AACAASURBVEFvbUbzejCi8aLX2vxeNE9+5bFlGCWnM4F8gv7kRM0Z0Otr6fjcx2f02pFf/a4guBpjxhIM3feQBFhCIAFWkaZ6O7v3F9eKUoBlnYU5Huds8ON2qiRK7MvX3uJg78HjF071eW1cfdlEDapav85f/skadu6JceBwgrWra1ixLP+h+6OfHeGhxwYZyzF/6fUIr70Z4c8+W7gnXzZnkkyaeDwa2gyT4geGygeT3WVG7d53Sxt9Ayle2RYZL+/Q0ergI7d1omkKDz0+yHCwfHClqXD+WXNXuX4qdu+L89Nf95JMTvwMh4NZfvLLXk5b6ZnW9kinqqbb3klk8/MYkcLdsDxnn0HjuyrWIp4xd2AV7sCqab1Gb6gj8JNv0vvP/0bizaM1tC45n7Y7by+aRqu97nIiz75YFMg4AytpuPVGdr7zo6UvUibwSWzrYvR3m6m/8Sq8l5xH6Lebi57ju+QCVKcDAFdgFZ6Na4tyqgA8Z52BrbZ8ADjXLMsieP+jhDZtAdOk5sKzyfQOlH1+dmBuysoIcaKTAGuSay5v5PWdUWLxwhyJtWtqeMs5hUvP9x2Kk86W/kC162q5mbQCkWiOT35hO26nSnubk6svbchXeV9TU1CKoKcvyePPjDB5Ad/+w0l+8XA/n/jAUrI5kx/d28PL28JEojlamuy89aIGbrmh8tTJZJZlEY6Wr6Be4yk9fabrKnd/ehXd/Tmee2EIn1fjqksbx0foevvLB20KcPVlDZy7cX4DrE1bRgqCqzGJpMmmLUEJsKag9sqLWPpXf8LAD+/LryJ0u/C+5WyW/uXn5jTBfS7ojfUs++qfHPd5LR95L+mD3Qz/6rcYI/m8QNf6AM0ffBe73nsXiW1d07uwZZE5Gngs/5svsT+RIvL8q5DJgKZhb2+m9tpLsSwLRVFQFIX2z3yMg3f/r4KAxbW8k/bPfWx6154Fy7I4+IWvMXTvg+PB48gvf4tr/en54eYSo2z2tul93ghxslpcn36LwFnrfXzy9iX85okhDh1J4nConHGal4++r6OohMAbO6Jlq74HQxkCqzxTmiJMpU1SaZNgOMbu/XGyOZNrLy+cdnnupRDxROmLjU3LfefHh9n07MSORAe7U3T39qCpCjdfN/Xprp/8spc9JUbxABrq9bJJ7mPO2VDHktbiX61ygRnA1ZfVc+cdS+d9BWEyVeYHCCSS5Y+JQk3vvZnG97ydzJE+VI8bvWH+8+jmkqIoLPurz9N25wcJ/vYp9MZ6aq+5lB03fYTkrr3lX2fXsTLFo7RaQy31N1wBgL25kcBP/5md7/s0sWd+D4ZBpruPvXf9T5rffwvL/tfdKIpC3XWX41y5lMH/z959x8lVl4sf/5w2ZXe29002fTOpBBJIaCHU0AUVEBFFFOtVrtcLP73Wa7t2r+XaQFFEQRBFqdIlhBoIhEDCpm4223ufcuac8/tjkk0mc2ayfbO7z/v18mX2nDPf853Dlme+5XluvxezuQ3PzFIW3/xh+nzDzwEX6+2j9ps/pfuFV7H7w2QsWUjpx99H9imrXK/vevp5Wu55MHna88238VSUE91fn/g+s7Mouvodw+6fEFOJBFgu1q7J5/TVeXT3xPB6VXxe98AgXcCQ4de4+vIy9tSEeHvXofUWqpp++UQ06vDEhjbOXVuYEGzoeurFp5oGbR1RNr2evDjesmDDS+1cct7gkn329Jo8/Vw7bqs/fD6Vj76vgrzc4eXKOWVVHq9t7T5yAxVlJV4+fE3FhKRnqChPnQRy9syxTxA5lSiqmpwtfZLzlJdQ+qH3ANB0+71pgyujuID8d19M+98eTpwmU1UKL78AT9mhDzmtf3koHlwdzozRfOffyT1vLblnnwaAf8EcZn/jpoFLMoqy6BtmLTnHcdj1kc8lLPSP1jXS9+bbVP72BwSOX5L0msbf3JW04/Eg38I5ZC5fRPeLm7H7Q2QsrqTk+qvIPSIthBDTlQRYKSiKkjLn1Nu7evnBL6vZtqPbNWBSgFUrcsgKGHzt5koe/Vcr1TX9+P0ar7/ZTV2aqTKAxuYI4YiN33cogDvn9HwefLyZ9s7kT8dLFgbYuaePnl73X4QtbVFiMQfDOHoA88qWbtd7AGT6VVafMPwM3WedVkBDU4QnN7YN3GPeLD8fuGpGyiB2rF1yXjEvvdaVVKOxcm4GF7ks5BfTV7QxOW3DQZ4ZpSx55A94CvPJv2AdTbfdTXhPDVp2gNz1Z1B6w3sTru/e8JJ7Q2aMjsc2DARYo6nzsQ10b3w56bjZ0EzT7+4m8JOvJR5va6f35S0p29P8GVTe8h1iXT3Yff0YZcWSw06Iw0iANUQNTWF+fEs1Ta3uCUEzMzTWnpzHpefF/zh7DHXg3wDf+8WeowZYmRkaHk/iiFV2lsGVl5Zy5331A4GUAqw8LpurLi2jtd0kw6/S77KeKDdHR9cH94svP9dIOcqW4R/5t8s17yrnkvOKeHFzJ9kBHceBJze0cf+jTZSX+HjH+SVJdRHHks+r8V+fnsfd9zeyY3cvCrBwfoCrLy/DmybthJh+MpcvSjkEnXXKSjyF8c0qWSetIOukFWnbSlc3kRT1Ttv+8Ri7//IgPTur0fNzyTv/DMpv/NCgUyv0vr6NpEWcB0Sq65KONd3+V+z+1Escsk8/EYjvfiRHCq8LcSQJsIbowSeaUwZXq0/I5oPvmUlZceqppQvOLGRbVQ9dPanX9xy/LNt1998FZxVx3JIsntjQRiRqsWhBgNNOykNVFcpKvKxYksULryZPE558Qu6gP1nOneUnw68lLfIHWLZodOr/ZWcZrF9XxF8eaODeBxsHdhxu3trDa29287lPzXMtSTRW8nM9fOIDs45+oZhyQnv30/bXh8FxKLhsPf6F81Jem3fhWWSdsoqe5zYlHNeLCym+7soh3Tfr5BNod0vCqqrknHlK0uGWvz5M9c3fwjmQLDW6v57+LdswWzuY882bB3VPoyg/5Tk9L3lX4pG7QhOuH0RiVyGmO/mIPkTNKYIriP+hThdcARy3JJtPf3gOxy/LJidbxetRBnI/+X0qa9fkcf17ZqZ8fXmJjw9cOYOPvG8Wa9fkJyy8v/Zd5WRnJU615eXorDsl9S/WI/3y9hrX4KqsxMN1V6Xu11D19Jo88nTrQHB1UF1jhL8+lHoLuBCjpe5Ht7Ltouuo/9Gt1P/vb3jr0g+x/3/+L+X1iqKw4NbvUnj1O/DMnoleXED2macw78dfJWvV8iHdu/iay8k9PzlXVME7LyDvwjMTjtmhMDVf/sFAcHW49geeINrannTc9Z7vvQxf5dzkE14PeZecm3Q4sHJpysR0+ZecI2VxhDgKGcEaouys1I8sJ3twj3PVcTmsOi5nYEv227t6qa4JsWxxgJllwx+5ufPvDQklfgA6umL89q5avvSZBUd9fX1TmC3b3BfQegwVzyDWcA3Wsy930JFirdfu6uQkjEKMpu6XXqP+57cPZFsHsHt6abjlTgKrV5B37lrX1xm52cz70VfiJW1se9hpKBRdp/LW79J81z/oeWEziqqSve5kCt99IYqi4FgWkX11aFmZ1P/f7wfqGx4p1tJGz3OvUHDZ+qPeU/X7mPO9L7L/az+ib8t2cByM8hKK33s5RS6FpvMvOZeWu+5Pyn7vnT+b0o9fO6z3LcR0IgHWEJ11WgEvvtqZtNapuMDgorPTpy840sFpu0ULAixaMLLpt1DI4q0q9yH9bTt6ae+Ikp+Xfm1TTW2IkEvSVIDO7tigF8oPhs+T+tPvYNeLCTFcbX9/LCG4GhCN0vHw0ykDrIMUVY2vxxoBRdcpef+7Ewo8AzT/8W80334v/dt3oWX6XXf0DjB0PHMrBj6sHdTzyhuEqvaQvXY1vlnlA8ez1xzPkgd/T9czL2K2dpB/wTq0LPffPYqqUvnb71P73V/S89JmnEiUjOWLKfvUdfim2G5RIcaCBFhDtCyYxXVXzuDhp1rZVxtCUWD+7AyueWcZ2VmjX7JmsEIRO2VOp1DYpqfPIv8oqYkq52WSHdCSilcDFBV4RjXwWbsmj78+3OiafPTwBKtCjAUn7F6NAOJTciMVrqmj8da7iOyvw8jPo/CqS8g+eeVRX9f+z2eo+fqPsXvju1rdSuocTvV62P3Rz4HtkLliCYXXXE7Tr+6g56XXcaJRtNxs8s5fx9zvf3FgtE1RVXLPGlwqBS3Dz+yvfXZQ1wohEkmANQzrzyziqsvn8MSGevxelcWVgaQkpOMtL0enYoY/Kd0AwKwZPmaWHT2nU0Geh5OOz+HJjYlrOnQN1p2cP6pbsA1D5X3vKue3d9YmpIVYtijAte8qT/NKIUYusGo5rXc/4Hou0yUf1FD0bn6T3Z/8IpGaQzvzOh5+moov33jUheGt9zwwEFwNht3bT/TA9dHaBjqfei5hZM7q7Kb17gcwCvOp+OKnh/hOhi+8p4a6H91K7+tvoSgKmauWM/P/fQLvUQpyCzGVSIA1TIahcuI4l3VJR1EULjq7iFvv3J9Q+sXrUVh/ZiGaNrjg6OMfmI3fr7H5jS66eixKijyceUoBl5w3OjmhzJiNaTr4fSqnnpjHospMHnu6lb5+i3mz/ZxxSsGw6ycKMViF73kH7Q89Sfczifmosk5eSckQdwQeqf6ntyUEVwBWdw+Nt9xJ4ZWXoHpSj3SbTS0jurfrtCfx4sy2aaL5fRReczm+irH7EGN2dLHzhpsJvb174Fh49z7Cb+9m0d9uRcuQBL5iepAAawo567QCApkaT21so7U9Sl6uwZmnFHDqSYMvW6LrCh9+bwUfvGom4YiF36eNyuhcT6/JbX+u482qHsJhi1kzMrj43CJOPTGPqy+XESsxvlRDZ+FtP6T+/26n95UtOLZDYOUyym+8fqDg8nA4sRh9b2x3PRfeuZeuZ18m75zUSUQ9ZSWuBZ5RIOu0EzEUsHx+rO5eel96bdD9itY30nTLnQA033EfMz//CYqvfVdi3w8Ude54bAN2KEzG8iBlH7sWLTMDx7axevvRAhlHzbvVeMufEoKrg/re2E7T7++h/JMfGHS/hZjMJMCaYk46PpeTjh9+tvWDNE0hM2N0vj0cx+EHv6zmje2Hdihu29FLTV0Iv0/jhGXJOXiEGGuq38fMmz82ojbMzm7a7nsERdcpfNeFqD5v2p2Fmj998FZ09Tvo2vgy9hE5qAInHc+iP/+C4pIcWlp6qPnmT4cUYB0u1t5B3Y9uJe/CsxJqRtZ89Uc0/e7ugWSknY8+Q9dTL5B12iq6Ht9ItLEZo7SI/IvOZsZnP5Iy0IrsqUl57/Cu6mH1WYjJSAKsUWSaNo9vaKW+KUJ+brwost8vuWJeeaOLt6qS0z/09lk8/kyrBFhiUmr41R9pvPVOzIZ4CZ2G/7ud8huvJ3DictprG5JfoOuEdlWTfeqJKdvMPfd0Zn/9P2n6/V8Ibd+FmplB9skrmfX1/0wIaIqvu4K2ex/GbG5NbMAwwHRPf3I4s7GFlrvup/xT1wHQ92YVLXf9PSnTe9/mrfRt3jrwtdXZTX3VHpyoScUXPuXatpab+udZk4zvYhqRAGuUNDSH+cEv97Jn36HSEk9tbONTH5o94hQMk111TShVhQ5a2tKXDRLiWNT1/CvU/eDXCaVkovvr2f8/P2PuT75Bx6PP4oSOKDMTi1H7vV+RvXYN/rkV2OEIHU9uRPP7yVm3ZiBxZ9FVl1J4xcVE65vRAhnoLgGLr6KcWd+6mfof3Upoe7wAtW/+bEo+di1mYzOdT2wk1tZBrLsHO8VORDty6Gev46GnsPtSl8VJ4Di0P/gEM/7jBlR/8nqqwisvof3vj2H1JI7C6QV5SdOSQkxlEmCNkjv+UpcQXEE8K/kd99bxrc8HJ6hXYy8SsekLWeRk6ykXpxcXps6/lZuioLZtO/zrhTa2bu9BURROWJbN6avz0u5ktCyHp59rY8eePjwelTNPyWfB3MyhvSEhBqHtr4+41umzOrqp/94vkoOrg+fbO2m98x94ZpbSeOudA9NpGUuDzLj54+Stj+ffUlQVxeeh9Z4HUTwGhVdejJaZkdBWwcXnkH/+Ojr/9SJONEruOaejeuM/azNvik99Vn/xuzT/7i9J/VADmeRffM6hA8bQRtoj++qINjTjm5dcYirrxOOY+cVP0fjLPxLZVwvEg7/yf/8w/vmzh3QfISYzCbBGQThisX2n+6fEHbv72F8XomLG+NXWGw+RiM2tf9rPlre66emLUV7q46xTC7h0ffJuw9PX5PPgEy1JKSQ8hsLpJycvwLdsh//99V6e29Q5cOxfz7Xz2tZuPv3h2a5BViRq852f7eb1tw5NRT61sY0rLy3lnReWjuStCpHE7k2dnyp8IKhIpb9qJw23/BHM2KFjb1VR/YXvkHnCEjxFBdT9+Dc0/e4eYi3xlCkNv/wDMz77EYo+9b6EthRdJ+/c01Peq/zGD9GzaQuhN3ccOqipFF55MRmL5g8cKrzqUppuuwervdOllWRqhp+eTW/gnTPTdS1WyQeuoPCKS2h/6AkUTSP/4nMGgj8hpgupRTgKLMshZrnnW45ZEI6mmB+bxH7ym708ubGN1g6TSNRhb02IO+6t5bF/JW8z11SFGz88mxVLsjAOJCstK/Fy9eXlrDu5IOn6p59rSwiuABzgXy+08+Jm9z8A9z7YkBBcQTzB6t8faaK1LXX9SDG52VGT1r/9k+Y//i1pSmos+Ra41PQ7KNV8+AE9L2xOCK4OMuubaPrdPXQ8toH6n9w2EFwBRPc3sP+bP6MvzQLyI8W6e6j/yW3YZgwtNwe9pJDsdWuY+/0vMfuIAtG+mWWU/9t1iVndFQXPDPcPJ3ZfP3s/+zW2X34DYbf1ZoCW4aPoykvii/8luBLTkIxgjYLMDJ15s/y8sT35F/ycCh/zZme4vGry2lcb4rU3kxetmzF45sV23nflvKRzFeV+/vumSvbXh+nqNlk4PxOP4R7fb93uXg/RceC1rd2csip51CvVCGJ3r8XTz7dx5aVl6d6SmGSiLW3s+8oP6d7wElZHFwB1P/4tJddfRfm/XTfm9y/56DV0PPYMobd2JBzPXLkccOjb/Kbr6zyzZhA9IkfW4ayOLtofeBwnkvyhINbWQc2t91D42aPvfHRiMXZe/5/xYO4w0Zxscs46xXUUuOwT7yd73cm0/eUh7HCYrJNXkrv+DPZ94bsDa7oSb+LQ+8ob1Hz5Byz83Q+P2qfxYIUjNN56F+G9+zGK8yn+4FUYaRbdCzGWJMAaJZdfUEJtQyQhK3lmhsal5xVPucSZ23f1Eo64f0pvbU+/g6mi3EdFefpEg8NJGO84qSu2WXbaam5ikmm55wH2f+tnCSM8EB8BqvvhLfgXziPvvPS1BEfKyMli4e//l7r/vTWet0pVyFp1HDP+38fpfGwD1W/vxjlijVbG8UswigrSBli+yrl0P/tyyvOxbvcPH0dq/cvDScEVQHjHHhpvuZNZX/5319dlLqkk86ufSTg273+/Sv/Ovbx18XU4Llnme17YjNnWkZDyYSKE99Xx/Cf/i+7Xtg0ca7n7Aeb98Ctkn7pqAnsmpisJsEbJCctz+NJn5vPI0y20tkXJzTY4e20By4JTb1ty5ZwMPIZC1EwOXPJyRv4tddySLJ55oSPpuKrCyuPcP41Wzs1k247kUaxMv8ba1RP7i1+MHrOji9rv/jIpuDrICUdo/8ejYx5gAXhnlDDvB19KOl509TtQdI2WP99PpKYevSCPvAvOpPzG69n9yeTrD9KL8il+3zsxG5rpfPQZ12tyVi0bVN/6tr6d8lxoZ/Wg2jic5ve5jqoBWD29WN29Ex5g7f/WTxOCK4Dovjr2f/v/WHL/baNa6kuIwZAAaxTNnZXBJ6+b+rtk5s/JZPniLF59ozvhuKrCaSflj7j9M08p4PU3e9j4UgcHQzhFiWeqX3OCexLVKy4p5e1dvVTtPvQJW9dg/ZkFzCibWhsMprOWP/19IO9UKrGu8VuLlUrhFRdTeMXFScfz1q+l/cHHk9dp6RrzfvJ1VK+H0o9fS+fTzxPatjPhkqxTVzHzA++krePo6RS07NSpYfQ051LxlJegZQVcF8EbxQV4Z01sNQY7HKH3lTdcz/Vt2Ub/G2+TuWLxOPdKTHdjHmAFg8EvAZ+pqqoqHOt7jbW29ij3PNDIzr19eDwaC+b4ee/lZaOW8Xwy+fcbZvOrP+znjW099PZblJV4WXdyPpecVzTitlVV4TMfmcPq43N47a1uFEVh1XHZnLwyN+Wn0ECmzn/fVMmDjzezp6Yfr0fjlJW5rF458qz24thxeO6mVHxzZo5DT4Yn//Lz6X11Ky133z+Qd0rNyaL8Ux8k98yTATAK8qj8/Y+o/8lv6Xt9G6qmE1i9ghk3fww1TZb4wxW//120/vn+pESkis9L/jvOG3K/Yx1dOCkSmCq6Fv90NYEc28Fx2TgAQMzCCrvXaBRiLI1pZBAMBs8EpsT8TG9fjP/56W721Bz69Fi1q5e9Nf38902VGPr02pCZFTC4+ZPz6Ow26eg0mVHmS7lofThUVeH0NfmcvmbwI2I+r8YVl8hi9qks99zTafzlHa45qAC882dR8rH3uZ47FiiKwuxv3kzhey6l/eGnUFSNwvdciu+IESDfzDLmfT/1dOLhnFiMptvvja+5chwCJ59AyQevouKr/0HdD35FZO9+AIzSIko+eCV568+Iv85x6HziWbqeeQnVMCh494VkLnPP2de39e2UCUut/hB2Xz9aYOJyzmkZPjKOW0T30y8knfMvriRr1fIJ6JWY7sYswAoGgyXA1cB3gLHf1jPGHni8OSG4Omjbjj6efLaNC84a+cjNZJSbbaRMFirEaAusWELB5efTctc/4ttKD/Lo5Jx5KhWf+yS+mcd+kJ25fBGZyxeNuB3Hstj50c/T+c9/DRzreORpejZuovK33yf/gnW0/eMxrFCIgssvwMjLGXjd7hu/QvsDT8RzyQDNf/obZR9/PzM++5Gk+/gr56JlZ2G5LLI3CgtcM7qPt/JPXU90dzXhmkNpI7TcbEo/ek3a+pBCjJURfdcFg8FHgRKXU18BLgNuAnIG215eXga6fmzW7mtqTTH8DNQ3xSgqmnqL2UdCnkd68nzSS/d8Cn//HapXL6flsWex+kJkL1/IvJtuwJ8iZ9NwWaEwTQ8+hZ7pp+j8MwZK2Uy0w59N7R/uSwiuDup8/FlCDz3O7BuuouTTySN61b/4E+33PZpwzO7tp+nXf2LeNReRfWTwV5RF8/rTabj3kaS2yi8/l+LSiZ+KL3rHOooX/pZ9v/gToX11eIryqfjgu8k/PXXtx+lIfvekNtrPZkQBVlVV1flux4PB4ImACXyM+BShPxgMfh74a1VV1U631wB0dCRvAT5WaGrq5IGKEqOlZXDbp6eDoqIseR5pyPNJbzDPJ3DVZQSuumzg616gdxSfaePv76Hp1rsGptcyli5kxs2fGChlM1GOfDZ1jz2X8tqGJ54n47ILXc/VP7LB9Xisp5edv/krs750Y9K5sm99jkgkRteGF7G6ejBKi8i78CwKbrzhmPl+Llo0n5Iv/8fA1xYcM307FsjvntSG+2zSBWVjMm5aVVX1CvAKQDAYnAN8uKqq6jtjca+h2Lq9h5dfj++CWXNCLssWDT5aPW11Hhtf6khKTRDI0Dh37aRfvy/EtNCzaQstd/4ds7kVo6yEkg+8m8zjEneXdW3cRO23f56w5qj/rR1U/9ehUjbHirSjamlmA+xY6hF5J8U5PSvAgl9/m0hDM5E9NfiXVA5MOQohko3pyuxgMLgA+BTxEawvBYPBCVkF6TgOv76jhm/8eCcPPt7Cg4+38PX/3cWtf6pJm6DycCuX5XDlpaXkZh+KSQvzDa69opyKckkDIMSxrvW+f7Lzg5+l9e4H6Hr6BVrv/DtVH/gPOh5/NvG6ex9yXdBtNjTR/Lt7xqu7g5J7wTowXD4nayp5552R8nWZxy9xP+ExyD37tLT39JYVk33aiRJcCXEUY7ryr6qqahfxdVg3jeV9jualzZ08/kxrQuoZ03R49F+trFiazerjB7d+4IpLyjh3bSEbXmwnO8fH6hUBMvzHxroMIURqjm3T+Os/ETtQVuegWHMrDb+6IyEx6ZHXJFyf5txEyD3ndIrf/25a/ngfTvRAIlCPh+KrLyXvwrNSvq7s4x+gZ+Mmel58LeF44WXryV67eiy7LMS0MS22Vmza0uVaf9WyYNNrXYMOsABycwzecX6JzGUfYza93slTG9to7zTJz/Nwzun5nLhi4hfeimNDaOde+lNkN+/f+jZmaztGYTwliG/WDFKFUb7KNEWeJ4CiKMz55s3kX3wOHY88DTjkrl9HzuknpX2dluFj4R9/RuMtf6T39W2oHoOcdSdT9N7LJOO5EKNkWgRYliV16qayJza08Lu76+gPHYyi+9myrZsbrqng7NOOnfUyYuKoGX4Uj+Fa7kUxDBTjUKqRko9eQ+eTzxGp3p9wXebxSyl+3zvHvK9uHMeh87FnadmylQgqRe97J97Ddk1mn7KS7FNWpny9faAIcu/mrSiaSvbpqyl+/7uY8ZkbxqP7QkxL0yLAWlIZcK1tB7A0OPSyEYPlOA73P9rMpte76OmLUV7i5YKzi1ixRKq7jxbLdnj4qdbDgqu4UMjm4SdbOPOUfNQpVmxbDJ2vopzASSvo2bgp6VzWScej52QlXDv/V9+m4We30bdlG4quEzhpBRVf+BSq1zOe3QbANmPs/vh/0fHYMwMldpr/eB8zP/eJQQV8diTKjuv+I6GIdMfDT9Pz0mbm/+J/ZMRKiDEyLQKsc9YWsmlLF69sSaydt/qEHM48dexGOG67q5aHnmgZqKdXUxdm+85ePv3hOaw6ThaIjobm1ij7at2zeu+r7aetw6SoYPz/KIpjT8UXb2TPjV8hvHPvwDH/0oVUfOnTHgu2SgAAIABJREFUSdcGjltE5a3fw3GcCQ9AGn72uwPTf4fEWtup++Et5F14FkZ++qnwptvuTgiuDmp/8EnyL/0X+RelXqslhBi+aRFgaZrC5/5tPo881cxbO+KFYJcuzOLCs4vQxmh0o70zyrMvHypWfFBXj8XDT7ZIgDVK/D4Vn1ejP2QlnfN5NXze6VXCSKQWWLGYpY/8gebb7yXa0Ix31gyKr30nqs+b8jUTHVwBdD//qutxs7GFlrv+Qfm/pS+U0bt5q/sJy6b72ZckwBJijEyLAAtA1xUuXV/CpevdEs+Pvle2dNHV7Z5PprZBCo+OltxsgyULM5NGJwGWLgyQFZg23+JiELQMP2WfeP9Ed2NIHDN53dhB/dt3HnWULV2ZGGWcK2fYZgxF146JwFWIsSYf78dIcYE3ZYH5DL889tF0w3tnEpyfMfC1AixakMmHr5k5cZ0SYpRkLHUvwAzQft8/2X75DfTv2JPympwzTwGXgEbx+8h7x/pR6ePRtD3wONvf/TFeP+litq69guovfAc7JB80xdQmH+/HyIqlWVTOzaRqd3LCwhVLZZH7aCop9vGt/wry3Msd1DWGmVnm47ST8mRxu5gSyj59PT2bthB6a0fySQd6N22h+nPfZvHfbnEdGSq86hJ6XtxM633/hKgJgOL3U3rD1WSftGKsu0/HYxuovvl/BgpFx5rbCO/Zh9ncRuVvvj/m9xdiokiANUYUReGj11bwi9v3sbs6vgjb51VYtSKX972rfIJ7N/VoqsIZJ+dPdDfEFGF2dtP8+3sw2zrxV86h6OrLUD3G0V84BrxlxSy66/9o+OUdtNzxN6ze5A9tvZu30v38q+ScllzYWFEU5v7oK+Rffj5dT25EUTXyL19P4Pil49F9Wv5030BwdbjOp1+g9/W3xq0fQow3CbDG0LzZGXz3S4t48dVOWlqjLFsUYMHcCakWJIQYpK6NL7P35m8R3Vc3cKz17gdY8Jvv4y0rnpA+GYX5zPryv9P10JOEXAIszBiR6lpwCbAgHmTlrjuZ3HUnj3FPk0UOe46Hc0Jhel7cLAGWmLJkMdAY01SF007K4/ILSyS4EuIY59g2td/+eUJwBdD32lvUfvOnE9SrQzKD7pnktfxczNZ2an/wazqfeXHQNVbHg1aQIo2EquKdO2t8OyPEOJIRLCGEOKB30xb6tmx3PdfzyhYcy0LRJq7+6OxPvI+OTVuxOo/YNWtZ1H33F/F/ewxy153Cgl9/O20KivGSf+FZ9L78OkfWKwusWkbe+tQFqYWY7GQESwghDoj19IHtUrgUsKMmzgSX1iq99Bzm/fhr5JxzGt55s8hYuQy9pBCr67A1TlGTzsc3sP/bPz9qe1Z/mMbb7qb2h7fQ/fwro9pXx3Ho2vAS4Zo6MpYvRi+Kr5FUPAbZp5/E3B99RdI1iClNRrCEEOKAnLWr8c2fTXj3vqRzgeMWoxoT/yszb/1a8tavBaDtoSfZ/ZHPuV53tICp85kX2fel7xHZXQNAg8dD7nmnM//n3xrxgn7Hcai++Zu03PsIRON5vBS/j9yLzmbGv3+IzOWLRtS+EJOBjGAJIcQBqtdDyQ3vRc1KXC/pmVlG6SfTZ0yfCLFW9xqrAHa/ewmp3i3bqP7S99n18f8aCK4AnGiUjoeeou4Hvx5xv9r++jAtd90/EFxBfFF711PPYaXolxBTzcR/HBNCiGNIyXVX4JtbQeu9DxFr78Q7s4ySD1+Nv9J9gflEyrvoLOp+eAux1vakcxmLFyQdq//Z76j/2e+we/tTttm1cRMVI+xX59PPg8tCeyccof2BJ8hec8II7yDEsU8CLCGEOELOGWvIOWPNRHfjqDxFBRRedQmNt/wJYofqcXrKSyj52LUJ14b27qfhF3ekDa4AnNDIR5gc071MGACmOeL2xThybPS2faiRbhzDj1k4D1QJHQZDnpIQQkxiFV/8NL45M+n457+IdfXgmzeLkg9dTWDF4oTr2v7yIFZXcs3OI/ldRr6GKrByOR0PPpl8QlXJWrt6xO2L8aFEQ/j2Po/W18bB7QhG6x5Cs07CCRRMaN8mAwmwpinTtOnps8gO6Oi67OQRYrJSFIXia99F8bXvSnudY7nvjjycp6KM0o+NvBh2yfVX0fXUc3Rv3JRwPO+is8m/6OwRty/Gh7fudfS+toRjWrgbX90WQgvPcq1xKQ6RAGuasSyH2/9Sxyuvd9LeZVKY5+HUE/N47zvLZMu0EFNY/sVn0/TbP7suftdLisg98xRKP3I1GUsWjvheqtfDwj/8mMZb7qRn81ZUVSXr1FWUXH8Viip7qyYF20LtbXU9pfW1oYY6sTPyxrlTk4sEWNPMb++q5ZGnWga+rmuMcO+DjaDANe+UGolCjJbQ7hr6395J1srleCaoxM7hMo9bTNHV76Dp9r8kJP0MnLSChX/8CXpWYFTvp/q8lN94/ai2KcaR46DYluspBQesqOs5cYgEWNNIf8ji5dc6k447wAuvdHDVpWUyXSjECMW6e9j7H1+n69mXsXv70ApyyTtvLXO+84UJKxh90Kxv3ETgpBV0PrYBOxIh47jFlN5wDVqGb1CvdxyH5tvvpf3BJ4i1duCZWUbRNZfJtN9UpOlY/lzU3uakU5Y3CzuzcAI6NblIgDWNNDRFaOtw38HT0h6lpy9GXs7E/gEQYrKrvumbdDzy9MDXVlsnrX9+AC0zk9nfuGkCexZfr1Vw2XoKLls/rNfX/fAW6n9628COxdCOPfRseh07FKbw3ReNZlfFMcAsCaKFu1Fj4YFjjqoTLVoA6sSVjJosZDJ8Gikp8pCb7R5T5+caBDLlB0aIkYg0NNO14WXXc51PPYedLn3BMc7qD9H2l4cT0kEA2D19NP/hr8dUgWkxOqycMkLzTiOaP4dYoAgzt4LQnDXEiisnumuTgoxgTSOBTJ2Vx2Xz1MbkpIQnHZ+LoUu8LcRIRGrqsLp7XM/F2jqxe/tQ83LGuVejo/fVrUT217meC+3ai93XjxbIdD0vJi87UEBEUjIMiwRY08zH3z8LRYFXt3TT2R2jIN9gzQm5fOCKGRPdNSEmvcwllRjlJZj1TUnnvLNnoOVkTUCvRoenvATF78MJhZPO6VlZKF7vBPRKiGOXBFjTjGGofOr6OfT2xWhujVJa7CXDL1ODQowGLStAwSXn0HjLnYknPAYF775oUqco8M+fTdbJJ9D99AtJ57LXrj4mCmELcSyRn4hpKpCpE8iU//xCjLaKr3wGNTODjkefIdbSjmdGKQXvvpDSD1890V0bsTnf/jx7PvPf9G7aApaN4vOSc8bJzPraZye6a0Icc8bsL2wwGPQA/wn0AUuAtqqqqi+O1f2EEOJYoKgqM2/+ODNu+hiOGZvw1AyjyTdrBov/egtdTz1PaNdeslYdR+DE4ya6W0Ick8ZyCONzwDNVVVUbAILBoPwUCiGmDUVRUKZQcHWQoijknnMaueecNtFdEeKYNpYB1jVATTAYXAkUAD8bw3sJIYQQQhwzlJHkLgkGg48CJS6nvgLcDXysqqrqD8Fg8FzgS1VVVWemay8WsxxdlwXXQgghhJgUUpY/GVGAlU4wGGwCzqiqqqoKBoM+4muxPFVVVe7FjYCWlp5Jk6muqCiLlhb3fDfTnTyb9OT5pCfPJzV5NunJ80lPnk9qw302RUVZKQOssdwz/CQw78C/ZwO70wVXQgghhBBTxViuwboZ+FowGDweWAxcO4b3EkIIIYQ4ZoxZgFVVVVUH3DBW7QshhBBieNS+NvSuelBUzIK5OJ6Mie7SlCOZJoUQQojpwnHw1ryK0b4PxYmv2jFadhEpXSJFnEfZ5K3bIIQQk5DjOPRs2kLb/Y8T6+6d6O6IacZo3YPRtmcguAJQYxG8DW+hROT7cTTJCJYQQoyT/m07qP7i9+ndvBXMGEZ5CUVXXszMz31yorsmpgmtu8E1r4BqRTHa9hItXz7ufZqqZARLCCHGgWNZ7P3sN+h96TUwYwCY9U3U//x2mu/46wT3TkwbdprN/OnOiSGTAEsIIcZB+/2P0/fG9uQTMYv2h54c/w6Jacn257oed1CwstzyhovhkgBLCCHGQXh/fcpzsfaucezJNOQ4YJng2BPdkwkXLZyHrSRXTHF0AyureAJ6NHXJGiwhhBgHWSeuQPF4cKLRpHPeOTMnoEfTg968E6O9GjXSh6N7ieWUEZ1xHCiTYHzBtlDMEI7uBW10Cod72vehOslTgUosit5eQ6xw7qjcR0iAJYQQ4yL71FXkrFtD5+PPJhzXCnIpfv+7J6hXU5veshNf7RYUDoxcWVG05h4UK0Zk9okT27l0HAdPw5voHftRI704hp9YVimRWStBHVm9XjXkPlqqAFp/GzEkwBotkyCEF0KIqWH+L79N0XVX4Js/G72kiOx1a5j3w6+Ss3b1RHdtSjLa9h0Krg6jd9ahRPsnoEeD42nchqdxO1qkFwVQzRCe9r14a14ZeeNpAjRHHZ1RMhEnI1hCCDFOtAwfc7/9eRzHAdtG0UY2GiHSsC3UaJ/rKdWKoPa1YR2L2csdB72j1jWVgt7VQDQawvH4h928mTfzQPuJgaetGZgFc4bdrkgmI1hCCDHOFEWR4GqsKSqO7nM95ag6ti9nnDs0SHYMxQy5nlKtKEqoc0TNW7kziZYsxNY8h25p+IiULcPxH6PPZJKSESwhhBBTj6IQyy1Ha0xecxTLKsHxZ09ApwZB1XEMP1jJmyFszYOTIs3CUERnHIdZOB+jYx+OomLmzwXDO+J2RSIJsIQQQkxJ0bKlKJaJ3lmLaoZxVJ1YVgnhWcfwAndFIZY3E7WhK2maMJZTNqLpwcM53kyipUtGpS3hTgIsIYQQU5OiEqlYSaR0KWp/O44vC8cbGP9+WCae5p2okR5szYNZOC/tdFy0dAk49sAuQtvwY2WXEqlYOY6dFiMlAZYQQoipzfBi55TF/23F8DS8hdbbCjhYGfmYZYvj03JjQIn04t/zPNpha6eM9n1EZh5PLNWickUhWr6caOmSUc+DJcaPBFhCCCGmB8fGv3sjem/zwCG9vx2tr41Q5TrQPWlefPS29Y5aFDNMLG8GjicTbAvP/s0JwRXEF6t7GrcTy6tIn9dK1SZmxE2MCgmwhBBCTAt6WzXaYcHVwPFQB57mHUTLlw2rXa2nGW/ta6ih+Lopu3EbticTxYqgpci3pUV60DvrieVXDOueQ6FE+zEa3yayuxu/5WBlFRMtXTw5stlPYhJgCSGEmBa0/nbX/FKQOsP5UdkW3v2b0cLdh9qyoiihaMp7HeRp2IoSC2EWVYKS4mrHRutuRLEsYrlloA7xz7YZwr/7WbRQFw7xP/p6bwtqfyfheaemvq8YMQmwhBBCTAuOkuZP3lADlwOMtuqE4OqgwYQtWqQXtfZ1FDMcr4945Pmuerz1b6KGOlEAyxsgWlRJrLhy0P3zNFWhuQSPelc9Wlc9Vu6MQbclhkbGB4UQQowfx0Hta0PtagQ7uejwWDIL52C7LBZ3UDHzhldwW4mFR9QnBdDbq8EyE4+bYbw18fVbB4M1LdKLr34rWnfjoNtPXXvQQettGV6nR8qM4Kl7A9/ujfj2voTWVT8x/RhjMoIlhBBiXCi9bfjqtqD1taHgYHkDmIXzMUuC43J/x59LpGwZ3qbtqGY8MLI1D2bR/GGP5MQCxXiU7ShOcs3DpPvjPrKlmWHU3tZDOx0Bo2UXmpm8fkuxYxiN29G6m0AziBbOA8M9Yz2AckTglmCEhaOHw21Xpd65n2jpYqJlS8e9P2NJAiwhhBBjz7bw12xKmE7TIr2o9W9iezKxhjmCNFSx4kpiebMw2veiODZm3mwcb+bQGrEtcGxQdeysImI55RidtQmXuAVTqaYNHcAxDquL6Niofe0pb6/3tqAcGH3SW3cTmbECK39W0nVG8w60/g73t6B7MQvnpbzHWPE0bk/aVak4NkbLbszC+ThpgsXJRgIsIYQQY85o3eO+VsmxMNr3jVuAFe+MF7Nk0dBfF4virX0dracZxTKx/TlEixYQnrMGuz4znv7BimH7c7B1L3pvazxRqO7F9gYwXHYwAjiKOlC6J74jcQtqyD0wgsRATTNDeOu30p9bnriOzLYwWnaj4CTfD5VI2fJ4KolxpqUIHNVYGL29BrNk4Tj3aOxIgCWEEGLMpSpgDKDEIuPYk2FyHPx7X0DvaRo4pPa1ooa7CGs60ZkrOFg9UGvbh9GxL172JqsIs3AeVnYZ6rZH0aK9SU1bgeL4bj6XHYmDoUX7MNqqMYsWHNa3NrRIj/tbUdRxSQ/hKl1qiCmWNmJqvRshhBDHJMuf6zKWEmcPdYouBcexUaIhsGNpr1PC3ejt+1BCgw9ktO5GtJ7kESjVMjFa9wx8bTS9jb9mE0Z3I1qoE6O7Ed++TWiddURLF2NriclMLU+AyIwVEIti1G9LGVw5ioqdbqfjEe/Z0Tw4Kf7EO7oxYcFMLFDoetwyMjALZo9zb8aWjGAJIYQ4OsfBaN0dz8lkx7B8OZglQRxPxtFfC1h5FVgtu9H7EneuxdcCLUjxqsEzmqqI7qwhs7cbW/diZRfHa/clTJvF8FW/jNbThGqZ8eLP2SWEZ68BLf2fQ62/w3W6DUA9mEzUtjBa9yQteFctE0/LLkILz8L252C07UWJRbG9mUQL5+FteAu9uxE1zUie5c/F9mXjaa9OOmdrHmK5iVOsTkYuVqAA3WWnoBUompAF7hAvwK2FOhP6ZWseomVLplw5IAmwhBBCHJV3/2aM1t0D63/0nmb0nmZC808f3CJxRSE07xS8dVvQeltQbAvLn4tZvBA7UDCivuktu/DWvYGDgwJoZj9aWzWKFYsn0zz4HmpeTViMrtgxjM46HPVVInPWpL2H7Q2k3AVoH1iYrfa1o0WSpwAB1HA3WCZ2Zj6RzPxDfdr7Ap6O/Ud9j7Yvi2jpIrQjpv4cFMyCOa4ldcIzj8e/7+WEPFixzEIiM1cc9X5jRvcQqlyH0VqNGmrHUY148Wtf1sT1aYxIgCWEECItpb8Do31fUnChhbswmt4mOmvV4BoyfPFAxrHBcUZtFCXet+TRJb27ESXUhePPActMWD+VeF0TESsKWupahLG8mVjN+ej9iYu0HVRiufH1TI7hxVE0FCc5v5ejaknvV4n2oXe79+lwtuEnWrQAx5dNaMFajKYdaOFuHE0nljMjZdFoJyOP/uC5GG3VKNE+AiVl9GiFR83erphh9La9KDiY+bNHfzG8omIWzQPGfxfjeJIASwghRFpGVz1KinVNR265HxRFHVyq88E2l6Len2LH0PraiPlzUGIRFNN9Ck6JhVHMCE6aAAtFJTxnNb79r6H1tqI4VjyPV8FcYoVzAXB82ViBQtdAzsoqTg6wQt2oVjTpWoinbrA9mdj+XMziSpzM+Cif4w0QnbUydT+PpGqYRfMB0IqyoKXnwL07MVqrUewYdkYeZuFcUFSM5h14Gt9GPZBA1WjagVm0YNh1GqezMQuwgsHgKuDzwCvAGuD7VVVVL4zV/YQQQowNJ91I0zGw88sx/OCyS9FRdKyMvIFrbG/AdWed7c0a1Foyx5dNqHIdSrgbxQxjZxYkBU3hihPi67wO1D10ULCyiojMOD65PX8utuZxDbJsfy79i84bk1qBevNOvA1voh5MQtoGekcN4bJleOrfRD0smFatKJ7Gt7Ey87Fyyke9L1PZWP5kfBO4s6qq6rvAHcDXx/BeQgghxoiZPxfL8Lues7JKxrk3yWJ5Fa7Lz2PZxTgHAixUjVj+rKTrHCCWP2tI05WOLxvbZUTq4LlQ8GzCc04mUraU0PzTCC1YB4Y3+VqPn5hL0OIAZl7F2BRiNiPxTPZHZHjXe1vw1b6WEFwdpGCjd9QmHRfpjeUUYRNQdODfRcCrY3gvIYQQY8XwEi1fhrdu68DUkYNCLHcG0dJhJOwcZWbxQhQ7hrdzP4S6sTUPVlYJ4SPWhkVLl+CoOnrHftRoP7bHTyy3YvRL9ShqPGgbhMisVaBqaN0NqGYE25tJLK9ieIlQj8LqaCRjx4aBMkFHUl1K8xyUaop4YOdktA/nwJTpRO1QPNYojpMqM8nRBYPBRwG3jy9fATYDfwZeBE4BbqyqqkobZMVilqPr8h9GCCGORXa4D6t2B1gmakE5asEMlLEYZRkmOxYltm8bTn8XquFDnVGJlpXveq3jOMdU3x0rBmYEPH4UdfQnl+zeDqKvPgrhvtQX+QIQdt8FqS1YhTE/cZrT7mnH3PoMTs+hhf9KVgHGijNRM3OT+xDqI7bndZyeNlA11LxS9PnHo0zugCzlN9GIAqx0gsHgZuDfqqqqXggGg8uBJ4DSqqqqlDdsaekZm86MgaKiLFpa3LPkTnfybNKT55OePJ/U5NmkYVtk176E3Vo78BfP1gyipUvGrZj0scxb8yqe1t1pr4kUzkOL9KIfkVA1lpFPqHJdUp4q/64N6N2NSe3EsssILVibeNAMkbHr2aRNEWZ2GeH5p4/NdOgQDPdnq6goK2XHx3KKsAJoOPDvBiB5AloIIYQYBZ7G7TiHBVdwIMFn49vxNVqDTIg6VSmRNCNXQMyfh1m2lKhq4Gl4C62vFQArI49oaXISUMUMofW2ural9rWixCI4+qE/+56mHa47TvXuBrTOuvGtRTlOxjLA+ijw7WAw+AawBLg+3eiVEEIIMVwp/9hbEYy26nim8OlMT52CwgwUxxOyHrgmOphEpLYV/58LxUo+px6W7DThWuL/7STAGoKqqqr7gPvGqn0hhBBiwBHlaQZ9bjKzY4AyqEXlZsEcjO4GOGL3oOXJJDz/1LRJVt04nkysjLykxKsAVkZuPHXG4bQ0fUx3bhKTRKNCCCEmPSsjD70veRTLVnXMvMHt6Jss1N42PI3b0PrbQVGwMguIlC2LZ6xPwcouRV94EpE9W9EiPTiAlZFPtHz5kIMrABSFaHEl6v7XEvJ42bqHaPHCpDVVsZwZ6J11SSvC47Uop2ZGdwmwhBBCTHrR0iX4Ip043YeKCDso8Tp3/uwJ7NnoUqJ9+KpfRIseWlOldtahhnvoD56TtmCyPmsxHd4y1J5mUHXswNHL5qRj5c8mbPjR2/aiRkPYHj9mwTzsrKKka2P5szH7O+LXHkj5YBt+IqVLR78UzzFCAiwhhBCTn+HFOPECerZtRg11xkvE5JZjHQujV7aF3lGDYlnxBKIuSUePpET6MBrio1SO7sXKKccsrsRo3pkQXB2khbvxNO8iWrY4fcOqhp1TNtx3ksTKKo6XAToaRSFScQLRwvkYnbU4qoqZP3dQz2KykgBLCCHElKAaHqLlS8fnZpYZ323X24riOFiZ8d12R+5W1Npr8Na/iRaN55fyNG0nWrQAszR1IKSEusioegrVPrReSu9tRg11pE74CShR9xxWxxLHn03UPz02HEiAJYQQQgyFY+Pf81xCvigt1IHW107/YWVxlGgfvppXEmv7mSG89W9h+3NS1vbz7Xk+IbiC+G47o73GtbTOQLf0qTsaNBlNfJVOIYQQYhIx2qqTknECaKFOPM07Br727t+csraf0V7j3ngsihZxH4lScHBUD7aWHEhZhh+zaMEg34EYDzKCJYQQQgyB6pKaYOBcuPvQv/s6UjcSDbm/PtqH4lq6Os72eAlXnIC36e34WjMO7AYsS56edG2/pxmjfR9KLIrtyyJaHJzS66AmkgRYQgghxBA4apo/nQfPOQ5KikSc8fMW3ppX4oGONxBPbWD4sL0BbNVImiKEA7siiyrB46c/byZqbxuoCnZGQfJuwINFmM0wtj+bWN4sYtVvkrH71UPruLpA76onNO80HF/WEJ+COBoJsIQQQoghMAvmYbRVJ+R/AnBQ47sEARQlPqIUTs5g7gBaf0fCSJjeWUdo3qk4/hxi+bOS6gY6xGv84TmQwFNRXdMhACh97fj3bUI7cG8HsFp2EYv2Jy2S18LdeBq3EZmzZvAPQAyKrMESQggxNhwbvXUPnrqt6G3VUyajuuPPJlK+HMs4NCVna16ipYuwcg8tQo/llrtO9jmKinrEGS3Sg7dhGwCRihOIFFViax4cwNY8RIsWEp5/2iA65+CrfX0guIL4Anm9rw1M92lJrS/1lKcYPhnBEkIIMeqUcDe+vS+hh+LrkOKjKLsJz12D4w1MbOdGQaxoPrH8Coy2apT+DlQrhhrqxFO39cB0n5do2TKUWBS9sxY1FsFRNGxflmvRYziwtstx0Hpa0PtaUawoCmArGo7hHVRSUDXUidbXNrQ3o8hYy1iQAEsIIcSo89W+PhBcwYFRlP42vLVbBjcSMxloHpRYFE/7fhQOjM511aN3NxCafzqOJ4PIrFVES5ei9bZg+QKokT4y9j6fuk07hrfm1YG8WQBaLITa8Ba2N4B1cAoylVgk7SJ5N1ZmwZCuF4MjYasQQohRpURDaL0truf0rno8dVsPFCoeA2YET91WfLs34tv7IlpH7djcB1Ci/Rgtuw4FVwdooU48jdsGvnY8PmL5FTgZeVi55Vg+99I9VqAAo3VPQnA1cC/HxuioiS+eD3WjRPtd27ADRVjeFAvWM3Oxj6g7GMsoIDJeyVmnGRnBEkIIMbosE1LsoFNw8DZtR+ttIbTgDNBG78+QHe4nY9czCVNwesd+ov1BojOOG7X7HGq7Jmmh+0FaqhQNikqkdAm+2tdQY5GBw5Y/l2jZMowjFrcnvDTUTcbbT6CGOkDRsAKFhGcch5ORd+giVcMsmo9atxXFOfTfwNa9eIOr6Qir8d2Flontz8EsnA+qNrQ3LgZFAiwhhBCjyvEFsP25KdcaAeh9rXia3iZavmzU7hvb81rSPRUcjNY9mIXzcbyjXFTYSj0K5yhKvJxO/Vb03lZwbGx/LpGSxVj5s+jPyMNo3X0gH1V2PEmopmP583CIT6keSYv0Hpr+cyz0nib81S/Tv+icQ+khALN5x9cyAAAUKElEQVR4IbYnI57vyoxgezMxC+eTUVSB09JDdObxo/schCsJsIQQQowuRSVauABf3RYUl3xOB6VL2DkcTrf74m7VimJ07CNaOko18BwHT90WjLbqlJdYmQX4dz+H3ntYOZ1wN2p/B6EFa3F8Wa6BjpU7EytQhH7EFKutaKhO8qigFu6KB5DFC5PbyZ05xDc2OhQzjBLtw/Zlg2ZMSB+OBRJgCSGEGHWxonmEDD/e2tfRoj3uF4327rU0u+wcZfSmwYyWnXiad7iOMjmAFSjG9ufiadmZdF6L9GA07yBasfKwFzkYLTvR2/ejmP04mpeYNwvVigAKVkYBihmKTw26UELdrsfHXczEu/9V9O5GVCuKZWQQy5tJdMaKQe2AnGpkkbsQQogxYeWWEZp/SsrM51ZW8ajeT80rdT1uGz7M/Dmjdh+ts941uAIw8+cSqjwDNdyV8ho1nBhwGo3b8dZuQe9vQzND6OFOtEgPaiyKEougOLG0qS2Mjv1oqWobjiNfzSY8h61L08x+PM078DS8OcE9mxgSYAkhhBgzjj+XaEkwIchyADN35qgXJ9bnn0AsqyQhSYGteYiULh3VenupFrZDfP0Zigpqmqmxw6fNbAujY19SagXlsP/Xe5rBDLkWeQZQbRNv3RvxzQUTRIn0ofU0JR8nnqUeZ2ipI6YCmSIUQggxpqJlS4lll6G316A4FrGs0njG81GeNlI0ndCCtejtNWh9bTiqjlkwF8fvnhZhuGyve7JQR9GIBeLla8yi+ehte9COyJ7uoGLmHsplpZgh1HByWoYjaeEuIuXL8e1/zTXPlWb2x9dilQSH+nZGhRruQk0R4ClmGBwLlOkVckyvdyuEEGJC2Jn5RDPzx/5GikqsYA6xgjljdguzeAFabwtqLJxwPJZThh0oBMAxfERmHo+3/k20SHxK0NZ9mIXzsPIPBViO7sUxvPEgJA3VMtG7G9MmEVXSjKyNNdufh615XEf3HE8GjOIauMlCAiwhhBBiCKxAEeE5azCad6KGu0AziGWVJKWcsPIq6M8pR++oQbFMzLxZYPgSGzvwWk/7vvT39ARQ+1Pk1jrYVGcDFC8Cffx37jkeP7GcGXja9yYeR4m/72m4yF0CLCGEEGKIrOwSrOySo1+oasQK5qa9JFKxCsWKofc0odixpDxY8SClAm/zjrTt6OHOeG6xGcuP3q8xEJm1EjQNrasBNRbG9gQw82dN2LTlRJMASwghhDiSZeJp2YUS6cPRvZjFlThHjj6NFk0nPP80lFAXWl87mCH03mbUaAjH8GHmzSJWOBejqx4t3JW2qdHOLQbEF6jbsXgy03QjUapGpGIlzLBQLBNH90zrQtISYAkhhJh6HAc11IWjqjjerCFNUSmhLvx7X0wIZvSOfURmnYiV7Z4KYjQ4/hxi/hwAYiQnRTUL5qDWvXGUYs6jOBXnOHgat6F31MbzcxkZxHLLiZYtO2qg5Uj5HQmwhBBCTC1aew3eprdRQ52AghUoIFq+HOvADr+j8da/mTRSpEX78dS/RSirZMLWE5klQRxVxWjdixbqdA2l7KzBvcfB8DS8hadx26H7WF2ojV1g20Rnrhi1+0xV03fsTgghxJSj9rXj2//aQACi4KD3tuKtfhnMyNFeDnYMra/V9ZTW347an7q+4niIFVUSWryeaNnS5NxiOeVER2u9k22hd+xPCuLiea1q41OGIi0ZwRJCCDFlGK27D5SYSaRF+/C07CJavjR9A46TJimmAyTXA5wI8dxipQO5xaxAMbG8ilEbXVNiEdRIn+s5LdqHGunDPjCdKdxJgCWEEGLKUGKpR6mUWCjluQGagZWZj9rdmHTK9udiZ4xDLq9BsjMLiGYWoPR34GneidG6G0f3YObNwsqrOHoDaTi6B9vwoZn9yffVfdiGf0TtTwcjCrCCwaAKfAT4BnB2VVXVm4eduxY4gXi4v7uqqurXI7mXEEIIcTS2J/UfftuTup7f4aKli1FD3QnBha15iZQsOuZ2xWk9zfiqX0I9LGO83tVIJNKHWbpo+A2rOlZOKVrrnqRTsexS0D3Db3uaGOl3ygrgJSAhxA0GgzOBm4Cbqqqq/h9wQzAYrBzhvYQQQoi0zMIFWC6jK5YvG7No/qDasANFhCrXESmqxMyZQbRwHqEFa7HyZ412d0fMaKpKCK4AFMfCaN0N1sjWSUVmnkA0f85ADURb8xDNmxXPdyWOakQjWFVVVa8BBINJi+rOB16tqqo6OJH9AnAhsHMk9xNCCCHScfw5RGavxmjajtZ/aBdhpGx5YpHlo7XjyyJaccLYdXQ0OE7KRfdatA+9uyG+Lmu4VI3InNVEo2GUcCeOLwcnzQihSHTUACsYDD4KuKWr/UpVVdX9KV5WDPQc9nX3gWNp5eVloOuTJ3dGUVHWRHfhmCXPJj15PunJ80lNnk16RUVZUJQF8xfgmBFQFBTdw1g9Nce2sep34vR3o2Rko5VXoqjjM43oOA4RQ4cUA1XZBTloBYnvfHjfP1nA6KV/OFaN9s/WUQOsqqqq84fRbjP8//buPUaus7zj+Hdue8te7LXXa3yJk9jOExU5hqaoRFGrQKqmNLSUokqIFimCkrZA2/xRCgoRDcXQkLpKU6BQAVVbaIBWkUpUKzUlKbdALg2JKaA+qS8bvGnt3bU38XpvM3N2+sectWdnZ2fX3jNz5vL7SJb3vOedmWdfvXvmN+c27ClZ7geOrvagycnlJ9M1qqGhPsbHp1bv2IY0NtVpfKrT+KxMY1PdyuOzhtszXIbE3BRdI0+SLrl7+tyJHzF31WspdNUnCHf2bKZjdvnvnO8ZZCrog5Lx0PxZ2eWOTbVQVquYfRi4wcwWrxe9EXikRq8lIiJSd50vHlkSrgDSM5N0jT5Xtxrmt11Pvndoyb3dg85estuvb8svWG4k672KcCPwHmAAuMPMHnT3J9x91MwOAvebWQB8zt11/pWIiLSERH6e1PnxiuuS0xMkcnO1++7CUplOZvfeTGryJKnZlyiku8gNXVP83kCJ1XpPcp8EDoT/ytd9Efjiep5fRESkIQV5EitcpZcI8vW903kiQTB4JQGNd5VjO2usG3qIiIisRWFh6R3Xgyz540foGD1CavJklbuxR/TyHT0EPRsrrgt6NlDouKKmry+NT/sQRUSkaaTOvkDHxDGSc1MUUh0E/cPk+7bQOXqEfHaaTsIvtOkbZvaamyBVo7e5RILc0B6So8+SDHIXmhdSGXJDe3X+kyhgiYhIc0hNnqT7J8+QWDz8lp8nNT5F+swIyZJDcgkgPXWajv/9AdmdtbspZn7TVRQy3WQmjpPIzVLIdJPbfDVB/9aavaY0DwUsERFpCpmJ4xfDVYnkCuc7pafGyda4pqB/mKC/0q0ipd0pYImISFNIzk9f2gMKC7Up5BIlp8ZIv/QiicIC+YFtxT1cOoTY8hSwRESkKRTSnZA9v7yd4mHBcgvdG2pe02o6Rp+jY/woiTDsZSaOk9u0i/krXxN/yFoIyEwcJTl7jkIqQ27TNRS6++OtqYUoYImISFPIb9hOaubMsjBVyPRAbmZJe9BxBfNbr6tnecskz52mY+woCS7uSUtQIHNmhKB3C/lNV8VXXG6W7mOPL7lRaubMCPPb95PffHV8dbUQBSwREWkKuWEjkZ8nM/kCydwchUSSoHeIuZ03kJw5S+/MKbIzMyx09ZLdci2Frnj3xmReGl0SrhYlgPS5U7EGrM7/+9Gyu9Angywdp35MfnCnblQaAY2giIg0h0SC7I79ZIevIzU9wUJHD4XwXlRBVy8d9kpebqjv2qtyL64a36drNanzZyq3Z6dJnz2pvVgR0I1GRUSkuWQ6CTZsvxCuGlW+b3jFiBX0bqprLctVC39B/cpoYQpYIiIiNRBs2EF+w85l7bn+reQ2746hoouCnsHK7Zke8oO76lxNa9IhQhERkVpIJJi7+mcJJraQmjoNhQJB72ZyQ3sgmYq1tOzWnyI1M0lq7uULbYVkmtzwtZDKxFhZ61DAEhERqZVEktzQbnJD8e6xKlfo6mVm7810jD1f/NqhdIbc4C4W+rbEXVrLUMASERFpR5lOstv3xV1Fy9I5WCIiIiIRU8ASERERiZgCloiIiEjEFLBEREREIqaAJSIiIhIxBSwRERGRiClgiYiIiERMAUtEREQkYgpYIiIiIhFTwBIRERGJmAKWiIiISMQUsEREREQipoAlIiIiEjEFLBEREZGIpdfzYDNLAu8CPgK83t1/GLa/BrgTeBYw4Cl3/+w6axURERFpCuvdg7UfeBKYKWt/BfCAux8E3g3cZ2ab1/laIiIiIk1hXXuw3P1ZADMrb3+4rGseyK3ntURERESaxaoBy8wOA8MVVn2oQpCq5L3Ax9z95dU6btzYQzqdWsNTNoahob64S2hYGpvqND7VaXxWprGpTuNTncZnZVGPzaoBy91vvdwnN7O3AVe4+4G19J+cLD/S2LiGhvoYH5+Ku4yGpLGpTuNTncZnZRqb6hplfJLnx+kYP0ZyboqFdAfBwDZyQ3sgkYi1rkYZn0Z0uWNTLZSt6xBhNWb220Cvux8ws33AvLs/X6vXExERiVtqaoyukSdJ5maLy0B66jSJ7DTZHa+Ktzipq3Wd5G5mG83sbmAAuMPMXhu2vwn4C+DXzOwbwIPAtnXWKiIi0tAyY89fCFeLEkDm7AsksnPxFCWxWO9J7pPAgfBfaftXKYYuERGRtpGcrXy6cTI/T+rlUfJDe+pckcRFNxoVERGJSrLyfosCUMh017cWiZUCloiISETy/ZUuuoegeyPBgM6UaScKWCIiIhHJbttHbmA7hZK316BrgPmdr4r9KkKpr5pdRSgiItJ2kinmdt9EcmqM9PkJFjJd5Ad3QbJ57vEo0VDAEhERidhC3xayfVviLkNipEOEIiIiIhFTwBIRERGJmAKWiIiISMQUsEREREQipoAlIiIiEjEFLBEREZGIKWCJiIiIREwBS0RERCRiClgiIiIiEVPAEhEREYlYolAoxF2DiIiISEvRHiwRERGRiClgiYiIiERMAUtEREQkYgpYIiIiIhFTwBIRERGJmAKWiIiISMTScRfQqMwsCbwL+Ajwenf/Ycm63wJeDQTAMXf/mwqPHwTuBY4De4G73P10PWqvNzM7BFxR0nQ9sM3d58r6jQAj4eKL7v6b9agvbmZ2D3BzSdNH3f3fK/RbdV61IjO7H5gBzgP7gTvd/VSFfiO0yfwxs18Afh0YAwru/uGy9V3AQeBFituXe939+boXGgMz2w0cAL4P7ADOuPuflvW5HfhdYHEb9Hl3/0I964yLmT3Bxd87cPdbyta389y5CngUOBk29QM/cPfbS/rcTkRzRwFrZfuBJylu+C8wsx3AHwGvdveCmT1tZo+5+/+UPf5jwNfd/Z/M7FcoTui316PwGPyDu38FwMyuAd5fHq5Cf+fu99S1sgbh7jdXW38J86oVTbv73QBm9n7gg8DvV+jXFvPHzHqAzwCvdPd5M3vIzG5x90dLut0J/MTd7zOzfcDngZ+Lo94YDAJfdvevApjZj83skLs/U9bvre4+Uvfq4vdvq/ydtPPcmQJ+x92/DmBmHwaWfdglormjgLUCd38WwMzKV90KPOPui3do/R7wBqD8jfA24KPhz48Df1+bSuO3GK5CfwB8YoWuP29mfwz0AY+4+3drXlyDMLMPAvNACviEu8+UdVnrvGo5i+EqlKS4J6uSdpk/NwIvuPt8uPw4xe1JacC6DbgLwN3/y8z2m1m/u5+rb6n15+5PlzUlgekKXd9rZqeAHuCT7n625sU1hn3hB5Vu4Gl3P1S2vp3nzhlgMVx1Aj/j7n9SoWskc6etA5aZHQaGK6z6kLs/vMLDtlBMwYvOhW3V+p0DNppZ2t3zl1tvnNYyVmbWD1xZeji1zAfc/anwE/r3zeyN7n60RiXXVbXxAf4ZGHH3aTN7N8UA+s6yfmudV01pjfNnA/CLwFtWeJqWnT9l1jIXVurT8m+SpczszcBhd//vslXfBA65+7iZ/TLFv8Fblj1Ba/p4+HeSAr5lZlPu/q2S9Zo7RW8DvlShPbK509YBy91vvYyHjQF7Spb7gUob+TGKn7RfCvtMNmu4gjWP1TuBv63yHE+F/8+Y2XPATVQeu6ZzCXPpMeB9FdrXOq+a0mrjY2YDwF8D71jp02Irz58yi9uORf1h26X2aWlm9jrgdRQPeS3h7idKFh8DHjazlLsH9aovLiV/J4GZfZviGJUGrLafO6HfAN5U3hjl3NFVhJfuMHCDmSXC5RuBRwDM7BXhCYQAh8J1UHwjKN9N21LCiwJupeT3NLOkmV0Z/nyLmf1SyUP2AMfqW2U8zOzPSxb3EoaC0vGhyrxqdWa2GfgU8D53P2Fmbwnb23X+fA/YFR7CgHD7YWaD4V5iKNm+hOfRHGmHQzyLzOw2itubPwS2mtmNpeNjZn9mZos7EPYCJ9ohXJnZdWZWund8L3BUc2epMJx/191z4XJN5k5b78Gqxsw2Au8BBoA7zOxBd3/C3UfN7CBwv5kFwOdKTkT+S+BfKO52vAv4uJldC+ymeAJzK/tV4F9LziGC4tWEXwD2UfyEdI+Z/TSwDXjI3b9T/zJjkTezByiOwT6K8wpKxmeVedXqvkZxW/SP4TmPU8BDtOn8CffQ/R7wV2Y2TvEqp0fN7D7gLMWrkx8ADprZ3RTDZvkh55ZlZjcAXwH+E/gPilcwfwp4MxfH5xTwaTM7QXH+tOoFRuXOAW80s20U90ydpPh+dC+aO6XuYOmFNB+gBnMnUSgUVu8lIiIiImumQ4QiIiIiEVPAEhEREYmYApaIiIhIxBSwRERERCKmgCUiIiISMQUsERERkYgpYImIiIhETAFLREREJGL/Dy2gOqhulwQ8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans,  cmap='coolwarm');\n",
    "# plt.savefig('../../images/ch13/ml_plot_02.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian Mixtures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.mixture import GaussianMixture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GaussianMixture(n_components=4, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianMixture(covariance_type='full', init_params='kmeans', max_iter=100,\n",
       "        means_init=None, n_components=4, n_init=1, precisions_init=None,\n",
       "        random_state=0, reg_covar=1e-06, tol=0.001, verbose=0,\n",
       "        verbose_interval=10, warm_start=False, weights_init=None)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_gm = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 3, 0, 1, 3, 3, 3, 0, 2, 2])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_gm[:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_gm == y_kmeans).all()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_classification(n_samples=n_samples, n_features=2, n_informative=2,\n",
    "                           n_redundant=0, n_repeated=0, random_state=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.6876, -0.7976],\n",
       "       [-0.4312, -0.7606],\n",
       "       [-1.4393, -1.2363],\n",
       "       [ 1.118 , -1.8682],\n",
       "       [ 0.0502,  0.659 ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:5]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x=X[:, 0], y=X[:, 1], c=y, cmap='coolwarm');\n",
    "# plt.savefig('../../images/ch13/ml_plot_03.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0041, 0.9959],\n",
       "       [0.8534, 0.1466],\n",
       "       [0.9947, 0.0053],\n",
       "       [0.0182, 0.9818],\n",
       "       [0.5156, 0.4844]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X).round(4)[:5]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True, False,  True,  True,  True,  True,\n",
       "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True, False, False, False,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True, False,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True, False,  True, False,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True, False,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True, False,  True, False,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True, False,  True, False,  True,  True,  True,  True,\n",
       "        True])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred == y  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, pred)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xc = X[y == pred]  \n",
    "Xf = X[y != pred]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a1fb3b470>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x=Xc[:, 0], y=Xc[:, 1], c=y[y == pred],\n",
    "            marker='o', cmap='coolwarm')  \n",
    "plt.scatter(x=Xf[:, 0], y=Xf[:, 1], c=y[y != pred],\n",
    "            marker='x', cmap='coolwarm')  \n",
    "# plt.savefig('../../images/ch13/ml_plot_04.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(C=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0111, 0.9889],\n",
       "       [0.7273, 0.2727],\n",
       "       [0.9711, 0.0289],\n",
       "       [0.0401, 0.9599],\n",
       "       [0.4849, 0.5151]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X).round(4)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xc = X[y == pred]\n",
    "Xf = X[y != pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x=Xc[:, 0], y=Xc[:, 1], c=y[y == pred],\n",
    "            marker='o', cmap='coolwarm')\n",
    "plt.scatter(x=Xf[:, 0], y=Xf[:, 1], c=y[y != pred],\n",
    "            marker='x', cmap='coolwarm');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DecisionTreeClassifier(max_depth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08, 0.92],\n",
       "       [0.92, 0.08],\n",
       "       [0.92, 0.08],\n",
       "       [0.08, 0.92],\n",
       "       [0.08, 0.92]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X).round(4)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xc = X[y == pred]\n",
    "Xf = X[y != pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x=Xc[:, 0], y=Xc[:, 1], c=y[y == pred],\n",
    "            marker='o', cmap='coolwarm')\n",
    "plt.scatter(x=Xf[:, 0], y=Xf[:, 1], c=y[y != pred],\n",
    "            marker='x', cmap='coolwarm');\n",
    "# plt.savefig('../../images/ch13/ml_plot_05.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   depth | accuracy\n",
      "--------------------\n",
      "       1 |     0.92\n",
      "       2 |     0.92\n",
      "       3 |     0.94\n",
      "       4 |     0.97\n",
      "       5 |     0.99\n",
      "       6 |     1.00\n"
     ]
    }
   ],
   "source": [
    "print('{:>8s} | {:8s}'.format('depth', 'accuracy'))\n",
    "print(20 * '-')\n",
    "for depth in range(1, 7):\n",
    "    model = DecisionTreeClassifier(max_depth=depth)\n",
    "    model.fit(X, y)\n",
    "    acc = accuracy_score(y, model.predict(X))\n",
    "    print('{:8d} | {:8.2f}'.format(depth, acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MLPClassifier(solver='lbfgs', alpha=1e-5,\n",
    "                    hidden_layer_sizes=2 * [75], random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 911 ms, sys: 6.35 ms, total: 917 ms\n",
      "Wall time: 264 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=[75, 75], learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "       random_state=10, shuffle=True, solver='lbfgs', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model.predict(X)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc = [tf.contrib.layers.real_valued_column('features')]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.contrib.learn.DNNClassifier(hidden_units=5 * [250],\n",
    "                                       n_classes=2, feature_columns=fc)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def input_fn():  \n",
    "    fc = {'features': tf.constant(X)}\n",
    "    la = tf.constant(y)\n",
    "    return fc, la"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.1 s, sys: 228 ms, total: 3.33 s\n",
      "Wall time: 2.39 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DNNClassifier(params={'head': <tensorflow.contrib.learn.python.learn.estimators.head._BinaryLogisticHead object at 0x1a2fc45fd0>, 'hidden_units': [250, 250, 250, 250, 250], 'feature_columns': (_RealValuedColumn(column_name='features', dimension=1, default_value=None, dtype=tf.float32, normalizer=None),), 'optimizer': None, 'activation_fn': <function relu at 0x1a2bca7048>, 'dropout': None, 'gradient_clip_norm': None, 'embedding_lr_multipliers': None, 'input_layer_min_slice_size': None})"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time model.fit(input_fn=input_fn, steps=100)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': 0.18742655,\n",
       " 'accuracy': 0.91,\n",
       " 'labels/prediction_mean': 0.501635,\n",
       " 'labels/actual_label_mean': 0.5,\n",
       " 'accuracy/baseline_label_mean': 0.5,\n",
       " 'auc': 0.97880006,\n",
       " 'auc_precision_recall': 0.9787836,\n",
       " 'accuracy/threshold_0.500000_mean': 0.91,\n",
       " 'precision/positive_threshold_0.500000_mean': 0.9019608,\n",
       " 'recall/positive_threshold_0.500000_mean': 0.92,\n",
       " 'global_step': 100}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(input_fn=input_fn, steps=1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 0, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = np.array(list(model.predict(input_fn=input_fn)))  \n",
    "pred[:10]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.86 s, sys: 993 ms, total: 10.9 s\n",
      "Wall time: 3.98 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DNNClassifier(params={'head': <tensorflow.contrib.learn.python.learn.estimators.head._BinaryLogisticHead object at 0x1a2fc45fd0>, 'hidden_units': [250, 250, 250, 250, 250], 'feature_columns': (_RealValuedColumn(column_name='features', dimension=1, default_value=None, dtype=tf.float32, normalizer=None),), 'optimizer': None, 'activation_fn': <function relu at 0x1a2bca7048>, 'dropout': None, 'gradient_clip_norm': None, 'embedding_lr_multipliers': None, 'input_layer_min_slice_size': None})"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time model.fit(input_fn=input_fn, steps=750)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': 0.09693404,\n",
       " 'accuracy': 0.97,\n",
       " 'labels/prediction_mean': 0.46790045,\n",
       " 'labels/actual_label_mean': 0.5,\n",
       " 'accuracy/baseline_label_mean': 0.5,\n",
       " 'auc': 0.99679995,\n",
       " 'auc_precision_recall': 0.9969604,\n",
       " 'accuracy/threshold_0.500000_mean': 0.97,\n",
       " 'precision/positive_threshold_0.500000_mean': 1.0,\n",
       " 'recall/positive_threshold_0.500000_mean': 0.94,\n",
       " 'global_step': 850}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(input_fn=input_fn, steps=1)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.6876, -0.7976],\n",
       "       [-0.4312, -0.7606],\n",
       "       [-1.4393, -1.2363],\n",
       "       [ 1.118 , -1.8682],\n",
       "       [ 0.0502,  0.659 ]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.2881, -0.5489],\n",
       "       [-0.3384, -0.5216],\n",
       "       [-1.1122, -0.873 ],\n",
       "       [ 0.8509, -1.3399],\n",
       "       [ 0.0312,  0.5273]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xs = preprocessing.StandardScaler().fit_transform(X)  \n",
    "Xs[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.7262, 0.3563],\n",
       "       [0.3939, 0.3613],\n",
       "       [0.2358, 0.2973],\n",
       "       [0.6369, 0.2122],\n",
       "       [0.4694, 0.5523]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xm = preprocessing.MinMaxScaler().fit_transform(X)  \n",
    "Xm[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.6791, -0.3209],\n",
       "       [-0.3618, -0.6382],\n",
       "       [-0.5379, -0.4621],\n",
       "       [ 0.3744, -0.6256],\n",
       "       [ 0.0708,  0.9292]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xn1 = preprocessing.Normalizer(norm='l1').transform(X)  \n",
    "Xn1[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.9041, -0.4273],\n",
       "       [-0.4932, -0.8699],\n",
       "       [-0.7586, -0.6516],\n",
       "       [ 0.5135, -0.8581],\n",
       "       [ 0.076 ,  0.9971]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xn2 = preprocessing.Normalizer(norm='l2').transform(X)  \n",
    "Xn2[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "markers = ['o', '.', 'x', '^', 'v']\n",
    "data_sets = [X, Xs, Xm, Xn1, Xn2]\n",
    "labels = ['raw', 'standard', 'minmax', 'norm(1)', 'norm(2)']\n",
    "for x, m, l in zip(data_sets, markers, labels):\n",
    "    plt.scatter(x=x[:, 0], y=x[:, 1], c=y,\n",
    "            marker=m, cmap='coolwarm', label=l)\n",
    "plt.legend();\n",
    "# plt.savefig('../../images/ch13/ml_plot_06.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.6876, -0.7976],\n",
       "       [-0.4312, -0.7606],\n",
       "       [-1.4393, -1.2363],\n",
       "       [ 1.118 , -1.8682],\n",
       "       [ 0.0502,  0.659 ]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [0., 0.],\n",
       "       [0., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xb = preprocessing.Binarizer().fit_transform(X)  \n",
    "Xb[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 ** 2  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 1],\n",
       "       [1, 1],\n",
       "       [0, 0],\n",
       "       [3, 0],\n",
       "       [2, 2]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xd = np.digitize(X, bins=[-1, 0, 1])  \n",
    "Xd[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 ** 2  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train-Test Splits "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(X, y, test_size=0.33,\n",
    "                                                    random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(C=1, kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_x, train_y)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train = model.predict(train_x)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9402985074626866"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(train_y, pred_train)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test = model.predict(test_x)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True, False, False, False,  True,  True,  True, False, False,\n",
       "       False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True, False,  True])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y == pred_test  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7878787878787878"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(test_y, pred_test)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_c = test_x[test_y == pred_test]\n",
    "test_f = test_x[test_y != pred_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x=test_c[:, 0], y=test_c[:, 1], c=test_y[test_y == pred_test],\n",
    "            marker='o', cmap='coolwarm')\n",
    "plt.scatter(x=test_f[:, 0], y=test_f[:, 1], c=test_y[test_y != pred_test],\n",
    "            marker='x', cmap='coolwarm');\n",
    "# plt.savefig('../../images/ch13/ml_plot_07.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.linspace(-4.5, 4.5, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xd = np.digitize(X, bins=bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 21],\n",
       "       [23, 21],\n",
       "       [17, 18],\n",
       "       [31, 15],\n",
       "       [25, 29]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xd[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(Xd, y, test_size=0.33,\n",
    "                                                    random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  kernel | accuracy\n",
      "--------------------\n",
      "  linear |    0.848\n",
      "    poly |    0.758\n",
      "     rbf |    0.788\n",
      " sigmoid |    0.455\n"
     ]
    }
   ],
   "source": [
    "print('{:>8s} | {:8s}'.format('kernel', 'accuracy'))\n",
    "print(20 * '-')\n",
    "for kernel in ['linear', 'poly', 'rbf', 'sigmoid']:\n",
    "    model = SVC(C=1, kernel=kernel)\n",
    "    model.fit(train_x, train_y)\n",
    "    acc = accuracy_score(test_y, model.predict(test_x))\n",
    "    print('{:>8s} | {:8.3f}'.format(kernel, acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>"
   ]
  }
 ],
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